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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
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 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 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 OSTI2022

Enabling Floating Solar (FPV) Deployment: Policy and Operational Considerations

Floating solar photovoltaics, or "floating PV", offer an opportunity for scaling up the deployment of renewable energy on inland and offshore bodies of water. Floating PV presents the opportunity to: increase clean energy generation to help meet growing demand; lessen the potential for land-use conflicts; help existing hydropower systems optimize their operation; and adapt to increasing temperatures and drought events by reducing the rate of evapotranspiration of waterways. There is significant promise for floating PV deployment in several Southeast Asian countries, especially those rich in hydropower resources, such as Laos and Thailand, but deployment remains limited. This slide deck presents an overview of NREL's research on (1) the policy, economic, environmental, and cultural barriers that currently exist, (2) recently completed modeling exercise on the potential operational benefits of pairing floating PV and hydropower, (3) international best practices that can be adopted to Southeast Asia, and (4) plans for high-resolution floating PV technical potential assessment.

Asia Clean Energy Forum↗
DOE OSTI2024

Simultaneous optimization of simulated moving bed adsorption and distillation for 2,3‐butanediol recovery

Abstract A combined simulated moving bed (SMB) and distillation separation scheme is developed to recover 2,3‐butanediol (BDO) from a dilute fermentation broth. The scheme was integrated into a lignocellulosic biorefinery that produces hydrocarbon fuels from corn stover with BDO as an intermediate. BDO recovery is one of the most challenging processes in this biorefinery; and given the high associated energy duties, direct distillation is considered cost‐prohibitive. An alternative separation is SMB adsorption in nanoporous materials, which can reject 90% of the water and reduce subsequent distillation costs. Rigorous models were used to optimize the SMB and distillation simultaneously. The separation can be added to the biorefinery while keeping the projected minimum fuel selling price (MFSP) below $0.66 USD (US dollars) per liter gasoline‐equivalent ($2.50/GGE, gallon gasoline equivalent). Finally, sensitivity analyses were conducted to assess the effects of cost and lifetime of the adsorbent, titer concentration, and BDO purity.

09 BIOMASS FUELS↗
DOE OSTI2020

Countermeasures for Preventing and Treating Opioid Overdose

The only medication available currently to prevent and treat opioid overdose (naloxone) was approved by the US Food and Drug Administration (FDA) nearly 50 years ago. Due to its pharmacokinetic and pharmacodynamic properties, naloxone has limited utility under some conditions and would not be effective to counteract mass casualties involving large-scale deployment of weaponized synthetic opioids. To address shortcomings of current medical countermeasures for opioid toxicity, a trans-agency scientific meeting was convened by the US National Institute of Allergy and Infectious Diseases/National Institutes of Health (NIAID/NIH) on August 6 and 7, 2019, to explore emerging alternative approaches for treating opioid overdose in the event of weaponization of synthetic opioids. The meeting was initiated by the Chemical Countermeasures Research Program (CCRP), was organized by NIAID, and was a collaboration with the National Institute on Drug Abuse/NIH (NIDA/NIH), the FDA, the Defense Threat Reduction Agency (DTRA), and the Biomedical Advanced Research and Development Authority (BARDA). This paper provides an overview of several presentations at that meeting that discussed emerging new approaches for treating opioid overdose, including the following: (1) intranasal nalmefene, a competitive, reversible opioid receptor antagonist with a longer duration of action than naloxone; (2) methocinnamox, a novel opioid receptor antagonist; (3) covalent naloxone nanoparticles; (4) serotonin (5-HT)1A receptor agonists; (5) fentanyl-binding cyclodextrin scaffolds; (6) detoxifying biomimetic “nanosponge” decoy receptors; and (7) antibody-based strategies. These approaches could also be applied to treat opioid use disorder.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Magnetic-Confinement Fusion—Plasma Theory: Tokamak Magnetohydrodynamic Equilibrium and Stability

Magnetohydrodynamics (MHD) provides a useful model to describe the crucial plasma macroscopic equilibrium and stability behaviors in toroidal tokamak devices by considering the plasma as a conducting fluid interacting with a surrounding confining electromagnetic field. MHD is the most basic plasma model, incorporating most large-scale phenomena, including plasma equilibrium and all major instabilities. MHD equations are obtained by taking moments of the Boltzmann equations for different plasma species. They provide a set of comprehensive physics constrains to compute and optimize the equilibrium plasma shape and pressure and current profiles that are critical to its stability and performance. In the ideal case, the equations have special properties that lead to efficient numerical calculation schemes, the most important of which is the ideal MHD energy principle for linear stability against small departures from equilibrium. In a tokamak plasma, equilibrium pressure is mostly destabilizing for MHD modes, whereas equilibrium current is also often a major driving force. Plasma resistivity creates new freedom for a MHD instability to grow, but there are also cases where the plasma resistivity plays a stabilizing role. Equilibrium toroidal flow and/or flow shear can affect MHD instabilities. Principal MHD instabilities include the internal kink mode, sawtooth, fishbone, external kink, resistive wall mode, resistive interchange, tearing and neoclassical tearing modes (NTMs), locked modes, toroidal Alfven eigenmodes (TAEs), and edge localized modes (ELMs). Fast-growing MHD instabilities can lead to an abrupt plasma disruption and termination that can potentially damage the device plasma facing components (PFCs) and in-vessel structures. Furthermore, an important MHD application is to develop robust techniques to mitigate and control MHD instabilities.

Dispersive pellet injection↗
DOE OSTI2025

Towards the next generation of Geospatial Artificial Intelligence

Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.

58 GEOSCIENCES↗
DOE OSTI2022

High-resolution laser-induced fluorescence spectroscopy of 28 Si 16 O + and 29 Si 16 O + in a cryogenic buffer-gas cell

Here, the electronic, laser-induced fluorescence spectrum of the B 2 Σ + ← X 2 Σ + transition in 28 Si 16 O + and 29 Si 16 O + has been recorded in a cryogenic buffer gas cell at K. Molecular constants are extracted for both 28 Si 16 O + and 29 Si 16 O + , including the Fermi contact hyperfine constant for both the B and X states of 29 Si 16 O + , and used in a discussion of the suitability of SiO in future quantum information experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Countdown to perovskite space launch: Guidelines to performing relevant radiation-hardness experiments

Perovskite photovoltaics (PVs) are under intensive development for promise in terrestrial energy production. Soon, the community will find out how much of that promise may become reality. Perovskites also open new opportunities for lower cost space power. However, radiation tolerance of space environments requires appropriate analysis of relevant devices irradiated under representative radiation conditions. We present guidelines designed to rigorously test the radiation tolerance of perovskite PVs. We review radiation conditions in common orbits, calculate nonionizing and ionizing energy losses (NIEL and IEL) for perovskites, and prioritize proton radiation for effective nuclear interactions. Low-energy protons (0.05-0.15 MeV) create a representative uniform damage profile, whereas higher energy protons (commonly used in ground-based evaluation) require significantly higher fluence to accumulate the equivalent displacement damage dose due to lower scattering probability. Furthermore, high-energy protons may "heal" devices through increased electronic ionization. These procedural guidelines differ from those used to test conventional semiconductors.

14 SOLAR ENERGY↗
DOE OSTI2021

Buffer concentration dramatically affects the stability of S-nitrosothiols in aqueous solutions

S-nitrosothiols (RSNOs) are an important group of nitric oxide (NO)-donating compounds with low toxicity and wide biomedical applications. In this paper, we, for the first time, demonstrate that the concentration of buffer remarkably affects the stability of RSNOs including naturally occurring S-nitrosoglutathione (GSNO) and synthetic S-nitroso-N-acetylpenicillamine (SNAP). For a solution with a high concentration of GSNO (e.g., 50 mM) and an initial near-neutral pH, the optimal buffer concentration is close to the GSNO concentration under our experimental conditions. A lower buffer concentration does not have adequate buffer capacity to resist the pH drop caused by GSNO decomposition. The decreased solution pH further accelerates GSNO decomposition because GSNO is most stable at near-neutral pH according to our density functional theory (DFT) calculations. A higher-than-optimal buffer concentration also reduces the GSNO stability because buffer ingredients including phosphate, Tris base, and HEPES consume NO/N 2 O 3 . In contrast to GSNO, the highest SNAP stability is obtained when the starting solution at a neutral pH does not contain buffer species, and the stability decreases as the buffer concentration increases. Furthermore, this is because SNAP is more stable at mildly acidic pH and the SNAP decomposition-induced pH drop stabilizes the donor. When the RSNO concentration is low (e.g., 1 mM), the buffer concentration also matters because any excess buffer accelerates the donor decomposition. Since the effect of buffer concentration was previously overlooked and suboptimal buffer concentrations were often used, this paper will aid in the formulation of RSNO solutions to obtain the maximum stability for prolonged storage and sustained NO release.

59 BASIC BIOLOGICAL SCIENCES↗