Search NASA⌕ Search

SEARCH · Search NASA

Results for “Natural Sciences”

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.

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

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Delivery of a Solar-Powered Forward Osmosis Seawater Desalination Plant: Trevi’s 500 m3/day Zero-Carbon FO Seawater Desalination Plant at NELHA

This paper presents an account of Trevi’s delivery of a 500 m3/day solar powered forward osmosis (FO) seawater desalination plant at the Ocean Science and Technology Park of the Natural Energy Laboratory of Hawaii Authority (NELHA). The project aimed to demonstrate the viability of solar thermal-powered desalination for agricultural applications through the integration of a 2MW micro-dish solar thermal array with a state-of-the-art FO system. Highlighted in the paper are the three distinct project phases; Planning and Design, System Construction, Installation & Testing followed finally by System Operation and Optimization. Results and decisions which led to the final plant design will be shared, highlighting how Trevi Systems succeeded in producing a zero-carbon FO seawater desalination plant with a projected Levelized Cost of Water (LCOW) estimate competitive with existing carbon-intensive RO technologies (based on some assumptions and the cost of heat which is required for FO.

14 SOLAR ENERGY↗
DOE OSTI2024

Heteroatom-Modified and Compacted Zeolite-Templated Carbons for Gas Storage

Methane, the primary component of natural gas, is an energy-rich fuel by weight but exhibits lower energy density by volume than diesel and gasoline; efforts to increase its “volumetric” energy density have been focused on the design and synthesis of a porous scaffold that can bind the methane favorably. Novel approaches typically invoke the highly designable class of materials known as metal-organic frameworks (MOFs) owing to the relative difficulty of controlling carbon structure and composition precisely, without the use of a metal-based node. In this project, we instead explore the concept of carbon templating to achieve a porous scaffold with atomistically thin walls separated at a distance of ~1.2 nm, ideal for the formation of precisely two layers of methane per pore. This bottom-up templating approach permits the use of any small molecular precursor to build up the carbon scaffold, begging the question: which other elements (besides carbon) can be used to tune the surface for optimal methane binding without compromising the structure of the overall material? Boron and nitrogen were both explored and nitrogen was identified as the ideal agent for increasing methane binding to the optimal strength for use at near room temperature. Densification of the resulting materials by mechanical compaction was then explored to produce free-standing pellets with the highest methane “delivery” capacities of any known material. Here we define the amount “delivered” as the amount stored minus the amount remaining in the tank at 73 psi, the lowest useful pressure for vehicular transportation applications. The key features of the resulting methane storage system are rapid discharging and refueling speeds, extremely long cycle life (infinite cycle life for high-purity methane), a near-constant heat of refueling (making the heat generated upon refueling easier to manage), and a low maximum pressure of operation (<1500 psi), enabling the use of all-metal Type I cylinders and significantly reducing the costs of conversion of existing vehicles to cleaner burning, abundantly available natural gas). The technical effectiveness of this approach was further improved by exploring several cost-reduction strategies and investigating the reliability of such storage systems in the presence of “real” natural gas mixtures which contain a mixture of gaseous species.

03 NATURAL GAS↗
DOE OSTI2020

Life-Cycle Greenhouse Gas Emissions and Human Health Trade-Offs of Organic Waste Management Strategies

Waste-to-energy systems can play an important role in diverting organic waste from landfills. However, real-world waste management can differ from idealized practices, and emissions driven by microbial communities and complex chemical processes are poorly understood. This study presents a comprehensive life-cycle assessment, using reported and measured data, of competing management alternatives for organic municipal solid waste including landfilling, composting, dry anaerobic digestion (AD) for the production of renewable natural gas (RNG), and dry AD with electricity generation. Landfilling is the most greenhouse gas (GHG)-intensive option, emitting nearly 400 kg CO 2e per tonne of organic waste. Composting raw organics resulted in the lowest GHG emissions, at -41 kg CO 2e per tonne of waste, while upgrading biogas to RNG after dry AD resulted in -36 to -2 kg CO 2e per tonne. Monetizing the results based on social costs of carbon and other air pollutant emissions highlights the importance of ground-level NH3 emissions from composting nitrogen-rich organic waste or post-AD solids. However, better characterization of material-specific NH 3 emissions from landfills and land-application of digestate is essential to fully understand the trade-offs between alternatives.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Recycling of natural fiber composites: Challenges and opportunities

Natural fibers have been widely used for reinforcing polymers attributed to their sustainable nature, excellent stiffness to weight ratio, biodegradability, and low cost compared with synthetic fibers like carbon or glass fibers. Thermoplastic composites offer an advantage of recyclability after their service life, but challenges and opportunities remain in the recycling of natural fiber reinforced polymer composites (NFRPCs). This article summarized the effects of reprocessing/recycling on the material properties of NFRPCs. The material properties considered include mechanical performance, thermal properties, hygroscopic behavior, viscoelasticity, degradation, and durability. NFRPCs can generally be recycled approximately 4–6 times until their thermomechanical properties change. After recycling 7 times, the tensile strength of NFRPCs can decrease by 17%, and the tensile modulus can decrease by 28%. The mitigation approaches to overcome degradation of material properties of NFRPCs such as adding functional additives and virgin plastics are also discussed. The main challenges in these approaches such as degradation and incompatibility are discussed, and an effort is made to provide a rationale for reprocessing/recyclability assessment. Finally, future applications of NFRPCs such as additive manufacturing and automotive part use are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Triepoxide formation by a flavin-dependent monooxygenase in monensin biosynthesis

Monensin A is a prototypical natural polyether polyketide antibiotic. It acts by binding a metal cation and facilitating its transport across the cell membrane. Biosynthesis of monensin A involves construction of a polyene polyketide backbone, subsequent epoxidation of the alkenes, and, lastly, formation of cyclic ethers via epoxide-opening cyclization. MonCI, a flavin-dependent monooxygenase, is thought to transform all three alkenes in the intermediate polyketide premonensin A into epoxides. Our crystallographic study has revealed that MonCI’s exquisite stereocontrol is due to the preorganization of the active site residues which allows only one specific face of the alkene to approach the reactive C(4a)-hydroperoxyflavin moiety. Furthermore, MonCI has an unusually large substrate-binding cavity that can accommodate premonensin A in an extended or folded conformation which allows any of the three alkenes to be placed next to C(4a)-hydroperoxyflavin. Importantly, MonCI, with its ability to perform multiple epoxidations on the same substrate in a stereospecific manner, demonstrates the extraordinary versatility of the flavin-dependent monooxygenase family of enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Nature-inspired lotus-shaped fins combined with hybrid nanoparticles and metal foam for high-performance latent heat thermal energy storage

Latent heat thermal energy storage (LHTES) systems play a critical role in renewable energy integration by providing high energy density and nearly isothermal operation during phase transitions. However, their performance is often limited by slow melting/charging rates, which motivates the search for enhanced heat transfer designs. This study investigates the melting behavior of RT-82 phase change material (PCM) using novel lotus-shaped fins combined with copper metal foam and conductive graphene nanoparticles and carbon nanotubes. A two-dimensional enthalpy-porosity model in ANSYS Fluent was developed to simulate the charging/melting process, capturing non-thermal equilibrium between the foam and PCM/nano-PCM. In this study, effects of fin geometry, nanoparticle concentration, and foam porosity on melting dynamics and cost-performance trade-offs were investigated. Results showed that natural convection accelerated melting by ~12% compared to conduction-only scenarios. Optimized lotus-shaped fins with higher fin density (T3F4 and T3F10) achieved up to 63% faster melting relative to sparse configurations. Graphene nanoparticles improved thermal conductivity, with a 6% volume fraction, by reducing melting time by ~6.9%, while their combination with 75% porosity foam achieved a maximum reduction in the melting time of ~51% compared to pure PCM. Cost-performance analysis identified T3F4 as the most balanced design, offering rapid thermal response without excessive material costs, while moderate-density designs like T3S6 provided economical alternatives with acceptable performance. These results highlight the performance enhancement that can be achieved by integrating bio-inspired fins, nanoparticles, and foams, into compact and efficient LHTES for solar heating, building thermal management, and industrial waste-heat recovery applications.

25 ENERGY STORAGE↗
DOE OSTI2022

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted by climate modeling studies and have been identified in several high intensity storms occurring over the last half decade. However, it has been difficult to detect historical trends in TC precipitation at time scales long enough to overcome natural climate variability because of limitations in existing precipitation observations. We introduce an experimental global high-resolution climate data record of precipitation produced using infrared satellite imagery and corrected at the monthly scale by a gauge-derived product that shows generally good performance during two hurricane case studies but estimates higher mean precipitation rates in the tropics than the evaluation datasets. General increases in mean and extreme rainfall rates during the study period of 1980–2019 are identified, culminating in a 12–18%/40-year increase in global rainfall rates. Overall, all basins have experienced intensification in precipitation rates. Increases in rainfall rates have boosted the mean precipitation volume of global TCs by 7–15% over 40 years, with the starkest rises seen in the North Atlantic, South Indian, and South Pacific basins (maximum 59–64% over 40 years). In terms of inland rainfall totals, year-by-year trends are generally positive due to increasing TC frequency, slower decay over land, and more intense rainfall, with an alarming increase of 81–85% seen from the strongest global TCs. As the global trend in precipitation rates follows expectations from warming sea surface temperatures (11.1%/°C), we hypothesize that the observed trends could be a result of anthropogenic warming creating greater concentrations of water vapor in the atmosphere, though retrospective studies of TC dynamics over the period are needed to confirm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Chemically Recyclable Polyester Thermosets from Activated Adipic Acid and Renewable Polyols

This study outlines a method for producing chemically recyclable crosslinked polyesters using renewable polyols-glycerol and sorbitol-combined with adipic acid (AA), which is transformed/activated into a polyanhydride mixture prior to use. A three-step procedure has been designed: 1) an acid-catalyzed reaction of AA with nontoxic isopropenyl acetate or acetic anhydride to form a crosslinking mixture (CLM) made of adipic-acetic mixed polyanhydrides; 2) a solvent- and additive-free process where glycerol or sorbitol, or a combination thereof, is reacted with the CLM to achieve a prepolymer, and 3) a casting/molding of the liquid viscous prepolymer to yield a thermoset as the end product. Different thermosets (eight examples) are prepared by changing the reagents ratio. These solids are thoroughly characterized by tensile tests, DMA, high-resolution magic angle spinning and solid-state NMR, thermal gravimetric analysis, DSC, and fourier transformed infra red (FT-IR) spectroscopy. The formation of cross-linked polyesters is confirmed in all cases, but mechanical properties varied significantly from one specimen to another. Interestingly, a tensile strength up to 18 MPa-approximately an order of magnitude higher than similar polymers-is achieved when sorbitol and the CLM are used in a 1:1 wt% ratio. The chemical recycle of the resulting polymers is achieved via methanolysis with quantitative recovery of the monomeric units.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Linkages between riverine flooding risk and economic damage over the continental United States

Economic damages from riverine flooding are expected to grow because of climate change. Yet, there are few 7 studies analyzing flooding damages in the U.S. that clearly measure the roles of hazard, exposure, and vulnerability 8 separately and locally. A lack of this knowledge prohibits spatially detailed predictions of future damages. By being 9 able to separate into these three risk factors, we provide all necessary inputs for uncertainty analysis of the flooding-10 damages forecasts that can incorporate new predictive scenarios for each component. To analyze the flooding risk 11 factors of non-coastal counties within the contiguous U.S. between 1999-2018, we gathered information on (1) 12 property and human damages from flooding, (2) maximum annual river discharge, (3) the number of housing units 13 and the years in which they were built, and (4) the incidence of flooding events. We used the method of trimmed 14 least absolute deviated and trimmed least square estimators to obtain the individual impact of hazard, exposure, and 15 vulnerability in the context of censored flooding damages for panel data. The resulting estimates indicate that 16 exposure has been the main driver of flooding risk for most counties in the U.S. Here, we use these estimates to describe 17 the main source of flooding risk for non-coastal counties for the 1999-2018 period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mobilization of mercury from contaminated creekbank soils

The industrial use of mercury (Hg) led to the contamination of numerous watersheds worldwide, including the East Fork Poplar Creek (EFPC) in Tennessee, USA. Mercury can accumulate in creek banks and floodplain soils and is mobilized into downstream environments due to erosion from precipitation and flooding. Here, this study aimed to evaluate the geochemical conditions contributing to the release of Hg from contaminated soils in this watershed. Bank soil samples from the EFPC watershed with total Hg concentrations ranging from 27.2 to 1,425 mg·kg −1 were used in a series of batch experiments with artificial creek water at a solid-to-solution ratio of 1:30 to assess Hg release. Additional experiments examined Hg release across different soil size fractions and solid-to-solution ratios, as well as the effect of dissolved organic matter and time on Hg mobilization. Mercury release ranged from 0.011 to 0.17% of the total soil Hg and is correlated with total Hg concentrations. Variations in release among size fractions suggested heterogeneous distribution of labile Hg species. Results indicated two distinct solubility regimes depending on solid-to-solution ratios. Dissolved organic matter enhanced Hg release, and time-dependent experiments showed that changes in mercury speciation could decrease dissolved Hg concentrations over time. Identifying conditions that promote Hg mobilization from contaminated soils improves our understanding of Hg fluxes into downstream environments. Key factors influencing mercury release include soil characteristics, water chemistry, and temporal changes in mercury speciation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Deep learning of free boundary and Stefan problems

Free boundary problems appear naturally in numerous areas of mathematics, science and engineering. These problems present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of free boundaries and complex dynamic interfaces. In this work, we propose a multi-network model based on physics-informed neural networks to tackle a general class of forward and inverse free boundary problems called Stefan problems. Specifically, we approximate the unknown solution as well as any moving boundaries by two deep neural networks. Besides, we formulate a new type of inverse Stefan problems that aim to reconstruct the solution and free boundaries directly from sparse and noisy measurements. We demonstrate the effectiveness of our approach in a series of benchmarks spanning different types of Stefan problems, and illustrate how the proposed framework can accurately recover solutions of partial differential equations with moving boundaries and dynamic interfaces. All code and data accompanying this manuscript are publicly available at https://github.com/PredictiveIntelligenceLab/DeepStefan.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Machine-learning predictions of the shale wells’ performance

The ultra-low permeability nature of shale reservoirs leads to an extended linear flow and necessitates horizontal wells with multi-stage engineered fractures to efficiently extract hydrocarbons resources. These artificially-generated and naturally-occurring fractures form complex networks that create complex flow regimes which control oil production. These fractures are neither identical nor equally-spaced, which leads to a production profile with a masked onset of the boundary-dominated flow. The combination of the extended linear flow with the indeterminate onset of the boundary-dominated flow challenges the current deterministic analytic approaches to forecast the estimated ultimate recovery (EUR). In this work, we propose a novel machine-learning approach which overcomes these challenges and provides reliable EUR estimates based on field-wide analyses. We implement a novel unsupervised machine learning (ML) methodology, which allows for automatic identification of the optimal number of features (signals) present in the data based on non-negative matrix/tensor factorization coupled with k-means clustering incorporating regularization and physics constraints. In the presented analyses, the input data to the ML algorithm is the available (public) production history from the field collected at existing unconventional reservoirs. We validate our approach through hindcasting of the production data, where we achieved an excellent agreement. In addition, our approach is able to identify the poorly-performing wells, which could benefit from early refracing. Our approach provides fast and accurate estimations of the well performance without presumptions about the state of the well or the flow regime.

03 NATURAL GAS↗
DOE OSTI2020

Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks

In this work, we compare hydrocarbon production curves obtained from a graph-based reduced-order model with the high-fidelity Discrete Fracture Network (DFN) predictions for a fracture network created using data from a real shale site. We observe that the bounds for the high fidelity DFN model lie within the bounds for the reduced order model, implying that the reduced-order model provides a conservative estimate. Moreover, we found that except for first-passage times and late arriving mass, the production curves from the reduced-order model predict transport accurately. However, it is to be noted that the results are inspite of trading a three-dimensional geometry for a reduced system in the form of a graph, one that is 500–1000 times faster in terms of computational efficiency (for this particular application). In addition, we also compare the production curves for large drawdown and small drawdown using our graph approach. The reduced-order model is successful in showing that the long term productivity is higher in case of small drawdown although the initial productivity is higher for large drawdown. Thus, this reduced-order model offers great potential in uncertainty quantification for production, as well as in providing operators with information to make real-time decisions for optimal production.

03 NATURAL GAS↗
DOE OSTI2020

Probabilistic evolution of stochastic dynamical systems: A meso-scale perspective

Stochastic dynamical systems arise naturally across nearly all areas of science and engineering. Typically, a dynamical system model is based on some prior knowledge about the underlying dynamics of interest in which probabilistic features are used to quantify and propagate uncertainties associated with the initial conditions, external excitations, etc. From a probabilistic modeling standing point, two broad classes of methods exist, i.e. macro-scale methods and micro-scale methods. Classically, macro-scale methods such as statistical moments-based strategies are usually too coarse to capture the multi-mode shape or tails of a non-Gaussian distribution. Micro-scale methods such as random samples-based approaches, on the other hand, become computationally very challenging in dealing with high-dimensional stochastic systems. In view of these potential limitations, a meso-scale scheme is proposed here that utilizes a meso-scale statistical structure to describe the dynamical evolution from a probabilistic perspective. The significance of this statistical structure is twofold. First, it can be tailored to any arbitrary random space. Second, it not only maintains the probability evolution around sample trajectories but also requires fewer meso-scale components than the micro-scale samples. To demonstrate the efficacy of the proposed meso-scale scheme, a set of examples of increasing complexity are provided. Connections to the benchmark stochastic models as conservative and Markov models along with practical implementation guidelines are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Soil moisture gradients strengthen mesoscale convective systems by increasing wind shear

Mesoscale convective systems are a class of storm linked to extensive flooding and other destructive hazards in many regions globally. In West Africa, soil moisture impacts provide a valuable source of predictability for mature storm hazards, but little is known about mature storm sensitivity to soil moisture in other climatic regions. Here we use a storm track dataset, satellite observations and reanalysis fields to investigate the response of mature storms to soil moisture in seven global storm hotspots—West Africa, India, South America, South Africa, Australia and the United States Great Plains. We demonstrate that mesoscale soil moisture gradients (~500 km) can enhance storms by driving increased vertical wind shear conditions, a crucial ingredient for storm organization, through the strengthening of atmospheric temperature gradients. This is evidenced by a 10–30% increase in precipitation feature size and rainfall for the largest storms on days with favourable soil moisture gradients compared with unfavourable gradients. Global simulations confirm that soil moisture gradients influence wind shear. The results demonstrate the importance of soil moisture feedbacks for accurate forecasting of mesoscale convective systems and future projections of extreme events under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Fast mass spectrometry search and clustering of untargeted metabolomics data

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An improved daily standardized precipitation index dataset for mainland China from 1961 to 2018

The standardized precipitation index (SPI), one of the most commonly used drought indicators, is widely used in the research areas of drought analysis and drought prediction in different fields such as meteorology, agriculture, and hydrology. However, its main disadvantage is the relatively coarse time resolution of one month. To improve the time resolution of SPI to identify flash droughts, we have refined the traditional SPI calculation method and developed a new multi-scale daily SPI dataset based on data from 484 meteorological stations in mainland China from 1961 to 2018. SPI data from three different sites (located in Henan, Yunnan, and Fujian Provinces) at the three-month timescale were analyzed by comparing with historically recorded drought events. We found that the new multi-scale daily SPI can effectively capture drought events in different periods and locations and identify the specific start and end times of drought events. In short, our SPI dataset appears reasonable and capable of facilitating drought research in different fields.

54 ENVIRONMENTAL SCIENCES↗