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On the convergence of statistics in simulations of stationary incompressible turbulent flows
When reporting statistics from simulations of statistically stationary chaotic phenomenon, it is important to verify that the simulations are time-converged. This condition is connected with the statistical error or number of digits with which statistics can be reliably reported. In this work we consider numerical experiments of low Reynolds number incompressible homogeneous and isotropic turbulence as a model problem to investigate statistical convergence over finite simulation times. Specifically, we investigate the time integration requirements that allow meaningful reporting of the statistical error associated with finiteness of the temporal domain. We address two key questions: (1) How long should a simulation be performed in terms of large eddy time, and (2) How should the simulation time be divided among temporal windows over which a quantity of interest is estimated so that its statistical error could be reliably reported? We find that reliable reporting of statistical errors requires simulations on the order of 10 4 large eddy times, which is orders of magnitude longer than typically performed. Additionally, data post-processing should employ windows of at least ten times the large eddy time scale, with the most robust computation of statistical error of the mean requiring window sizes of an additional factor of ten. For practical simulations, we demonstrate that it is possible to estimate the statistical error within a factor of two under a less stringent condition in which a minimum of four windows with size at least ten large eddy times are used. In conclusion, our observations for homogeneous isotropic turbulence are also shown to hold in turbulent channel flow.
Performance of sweeping gas membrane distillation for treating produced water: Modeling and experiments
Membrane distillation (MD) has shown promise for desalination of produced water (PW), but there are very few studies reported on the performance of MD with real PW and the characteristics of the fouling and scaling in MD. Among different MD configurations, sweeping gas MD (SGMD) is the least studied configuration. Here we report the application of SGMD for treating high-salinity PW (123 g L -1 ). The SGMD operating conditions were first carefully optimized through pure water MD experiments by varying operating conditions. The flux evaluation was accompanied by a detailed Aspen Custom Modeler simulation based on the electrolyte-NRTL thermodynamic model to better understand the heat and mass transfer in the feed and permeate channel. Supported by experiments, modeling results confirmed the partial condensation of the vapor in the permeate channel. Furthermore, it was concluded that the saturation of the sweeping gas is controlling the overall mass transfer through the membrane. Selected membranes and optimized conditions were then used to conduct short-term and long-term performance tests with PW. In long-term MD test, the concentration of the PW increased by a factor of two (50% water recovery), while the membrane exhibited small flux decline. Elemental analysis showed that strontium and sodium were the major scalant compounds.
Mineral Recovery Enhanced Desalination (MRED) process: An innovative technology for desalinating hard brackish water
Desalination of water containing high concentrations of calcium, dissolved carbon dioxide, sulfate, silica and other sparingly soluble salts is difficult because of the scaling potential. An innovative pre-treatment scheme was investigated at bench scale that selectively removes these constituents and produces a soft water to enable desalination with high feed water recovery. The process first removes dissolved carbon dioxide by air stripping at low pH. Magnesium is removed by precipitation at high pH. Calcium is removed by ion exchange (IX), and sulfate is removed by nanofiltration (NF). Cation regenerant from IX, containing calcium, and concentrate from NF, containing sulfate, is combined to precipitate gypsum. Concentrate from the desalination process consisting of a concentrated NaCl solution is used to regenerate the IX resins. The selective precipitation, IX, and NF processes were tested in laboratory experiments and produced magnesium hydroxide and gypsum at greater than 90% and 95% purity respectively. A process model was developed to calculate process performance, mass and liquid flow rates. Finally, the MRED process offers the following benefits: 1) greater recovery of brackish feedwater by a desalination process, 2) recovery of marketable commodities and 3) reduction in the volume and mass of waste products from the treatment process.
Striving to translate shale physics across ten orders of magnitude: What have we learned?
Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.
A cloud-based, open-source tool and database for stream solute tracer studies
Not provided.
FIND: A Synthetic weather generator to control drought Frequency, Intensity, and Duration
Water systems worldwide are experiencing climate change-induced shifts in drought properties like frequency, intensity, and duration, affecting water security and reliability. To develop and test effective drought preparedness plans, researchers often use synthetic weather generators to create hydrological scenarios that explore drought variability beyond historical records. Existing weather generators typically allow users to adjust streamflow statistics like percentiles or temporal correlation but do not directly control drought properties of frequency, intensity, and duration. To fill this gap, we propose FIND (Frequency, INtensity, and Duration) synthetic weather generator. FIND incorporates a standardized drought index to directly and in dependently control drought frequency, intensity, and duration in generated streamflow time series while preserving observed hydrological variability. Use cases for FIND include i) water systems analysis applications that seek to train and test drought strategies under historical and plausible future drought conditions, and ii) bottom-up vulnerability studies relating system vulnerability outcomes to specific changes in drought properties of frequency, intensity, and duration. Here, we demonstrate FIND’s versatility through three experiments: replicating historically observed drought properties, generating streamflow scenarios for multiple sites preserving correlation between their drought conditions, and generating a set of scenarios with direct and independent changes in drought properties. FIND source code is openly available for applications beyond the scope of this paper.
Model linkage to assess forest disturbance impacts on water quality: A wildfire case study using LANDIS(II)-VELMA
Not provided.
A filter-dependent granular temperature model from large-scale CFD-DEM data
The computational study of strongly-coupled, gas–solid flows at scales relevant to most environmental and engineering applications requires the use of ‘coarse-grained’ methodologies such as the two-fluid model, particle-in-cell approach or the multiphase Reynolds Averaged Navier–Stokes equations. While these strategies enable computations at desirable length- and time-scales, they rely heavily on models to capture important flow physics that occur at scales smaller than the mesh. To date, the models that do exist are based on a limited set of flow conditions, such as very dilute particle phase. To this end, we leverage a large-scale repository of CFD-DEM data to develop filter-size dependent models for the mean variance in particle volume fraction, a quantity commonly used to assess the degree of clustering, and the granular temperature, a key quantity for accurately predicting gas–solid flows. In conclusion, because of its filter-size dependence, the granular temperature model can be directly translated to coarse-grained approaches and tied directly to grid size.
Analytical simulation of meander morphology from equilibrium to long-term evolution: Impacts of channel geometry and vegetation-induced coarsening
This study introduces an innovative approach to modeling meandering river morphology, integrating and investigating the effects of geometric characteristics and vegetation-induced channel coarsening. The developed comprehensive framework combines several advanced techniques: Genetic Programming for refining the scour factor of transverse bed slope, a Leaf Area Index (LAI)-enhanced analytical model for quantifying vegetative flow resistance, and an upstream-weighted moving average method for efficient approximation of the convolution integral in meander migration calculations. The model is validated against both an idealized Kinoshita meander and a natural bend of the Tumen River (China) in equilibrium, demonstrating its robustness across diverse scales and conditions. The model's ability to simulate the long-term evolution, including cutoff events, provides valuable insight for river management strategies. The current findings demonstrate that channel geometry, particularly width-to-depth ratio, plays a dominant role in meander evolution, with wider channels prone to more complex and rapid morphological changes. Vegetation effects are most pronounced in channels with moderate width-to-depth ratios, where they can significantly influence migration rates and bed topography. A combination of channel widening and deepening, coupled with strategic vegetation management, can effectively enhance navigability while maintaining channel stability in the studied Tumen River reach. Sensitivity analyses highlight the complex interplay between hydraulic conditions, sediment characteristics, and vegetation in shaping river morphology. This research advances understanding of the multifaceted nature of meandering river systems and offers practical tools for informed decision-making in river engineering and environmental management, particularly in the context of climate change and increasing anthropogenic pressures on fluvial ecosystems.
Decomposing sources of value for electricity and negative emissions technologies in net-zero power systems
Deep decarbonization of the US power system would require rapid deployment of variable renewable energy (VRE) resources, which are projected to provide a substantial share of electricity generation at the time of net-zero emissions. However, the exact share of generation met by VRE and the roles of other technologies in supplying key electricity services—energy and firm capacity—remain uncertain. This study employs a detailed model of the US power sector to decompose the provision and value of electricity services, including negative emissions, by technology across a range of deep decarbonization scenarios. Results indicate that while technology deployment and the share of services provided by each technology vary significantly depending on future technological and market conditions, the value composition and future roles of individual technologies remain consistent. These findings offer guidance for research and development priorities and provide insights to inform electricity policy and planning.
Atmospheric clusters to nanoparticles: Recent progress and challenges in closing the gap in chemical composition
Aerosol nanoclusters (AN), defined here as molecular aggregates suspended in a gas with dimensions between 2 and 10 nm, are the link between substances that we think of as molecules, or the “gas phase,” and those that we consider as particles, or the “condensed phase.” The ability to measure and model the physical and chemical properties of size-resolved AN, which at present is rudimentary at best, is crucial for understanding how particles form and evolve in a number of environments that are natural or influenced by human activities. This review describes the current state-of-the-art for measuring and modeling the size-resolved composition of atmospheric AN. We focus specifically on instruments, many relying on mass spectrometry, that show promise for closing the measurement gap under atmospherically relevant conditions by increasing the size of measurable gas-phase clusters (bottom-up approaches) and by decreasing the size of measurable nanoparticles (top-down approaches). Theoretical methods for predicting AN composition have similarly relied on bottom-up approaches that extend the accuracy of quantum chemistry calculations to larger molecular systems, as well as top-down approaches that correct bulk composition aerosol models for size-dependent properties such as viscosity and volatility. Finally, current measurement and modeling challenges that must be overcome in order to close the gap are discussed.
Particle emissions from mobile sources: Discussion of ultrafine particle emissions and definition
There is no universally agreed upon definition for ultrafine particles (UFP). Commonly used definitions for UFP are either particle number below 100 nm or total particle number, but without an agreed upon lower cut point. For example, a lower cut point of 3 nm compared to 10 nm could result in a substantially higher count. Another definition for UFP is total particle mass but without a commonly agreed upon aerodynamic diameter upper cut point, e.g., below 100 nm, 200 nm, 300 nm, etc. Yet another definition is lung deposited surface area weighted by lung deposition fraction, found mainly in the particle mobility diameter range from 20 to 400 nm. It is clear from these definitions that there are inconsistencies in the way UFP is used and defined in the literature. Sometimes these metrics are well correlated, sometimes not. In this paper we suggest three exposure metrics: UFP-N, UFP-M, and UFP-S, that we believe will add clarity. These metrics represent total number, mass, and surface area below 500 nm, respectively. For surface area and mass, the 500 nm cut point can be either aerodynamic or mobility diameter depending upon measurement methodology. For all metrics, this cut point captures nearly all of the primary particle emissions from mobile sources. Furthermore, UFP-N would include a lower cut point of 3–6 nm and would not require an upper size cut point because there is very little particle number above 500 nm or even above 100 nm. Thus, our definition of UFP-N is consistent with the current definition of ultrafine number except for, importantly, the specification of a lower cut point. These exposure metrics can help facilitate consistency in the characterization of both short- and long-term UFP ambient exposures and associated health effects in epidemiological studies.
Evaluation of assumptions made by Hygroscopic Tandem Differential Mobility Analyzer inversion routines
The Hygroscopic Tandem Differential Mobility Analyzer (H-TDMA) measures the hygroscopicity of atmospheric particles, and many atmospheric processes that change this hygroscopicity also change the atmospheric size distribution. Two assumptions made during H-TDMA inversion create spurious hygroscopic trends as a function of the changing inlet size distribution. These two assumptions—that the particles exiting the first Differential Mobility Analyzer (DMA1) are singly charged and that the inlet size distribution has a slope of zero (flat)— generate Multi-Charge Dispersion (MCD) bias and Slope bias, respectively. First, we use a model, named TAO, to show that the inlet size distribution could theoretically change the measured ammonium sulfate hygroscopicity by 10%–20% as a function of diameter or experimental time with no change in relative humidity. Secondly, we show experimentally that aerosol emitted from the flaming combustion of grass creates MCD bias. Here, in this experiment, we measure the CPC response of the first three charges and invert these responses using a new routine named Junior. Junior's inversion of each charge shows that one growth factor distribution describes all measured diameters (no growth dependence on diameter). As in the modeling study above, previous publications of this aerosol system, using traditional inversion assumptions, report a decrease in hygroscopicity as DMA1 diameter increases. Unlike traditional inversions, Junior's inversion does not assume the particles are singly charged nor does it make the flat inlet size distribution assumption. Instead, both the inlet size distribution and each charge's CPC response are measured quantities. Thus, the discrepancy between our inversion results and previous publications is likely due to the traditional inversion routine assumptions. This underscores the importance for accounting for Slope and MCD bias during inversions. Experimental results should be carefully analyzed when reporting hygroscopic trends with respect to diameter or experimental time when using the traditional inversion assumptions.
Impacts of engine lubrication oil-derived ash on soot oxidative reactivity on a catalytic gasoline particulate filter
Characterizing soot oxidation kinetics is crucial for understanding how gasoline particulate filters (GPFs) perform both in terms of filtration efficiency and pressure drop. The most common method for measuring soot oxidative reactivity is thermogravimetric analysis (TGA). Because TGA is an offline method, it may inaccurately predict how soot oxidizes on a GPF, especially if a catalytic washcoat is present that may enhance oxidation rates through surface interactions as the soot loads on the filter. In this work, a novel in-situ soot oxidative reactivity measurement method was developed. The method involved loading a catalytic GPF under high temperature conditions conducive to soot oxidation and measuring the filtration efficiency of 100 nm particles. A correlation was made between filtration efficiency and loaded soot mass on the filter to allow calculation of the soot oxidation rate. The method was evaluated in experiments using a 2.0 L gasoline direct injection (GDI) engine with three oils of varying additive packages, including an oil with a high zinc dialkyldithiophosphate (ZDDP) concentration, a non-additive, pure poly-alpha olefin (PAO) oil, and an oil with a high concentration of calcium sulfonate. Calculated specific soot oxidation rates ranged from ~0.06–0.6 min –1 and showed strong dependence on GPF inlet gas temperature and lubrication oil additive type. The results clearly demonstrated a catalytic effect of calcium-containing ash particles. Reactivity of soot produced by the engine running with the high calcium containing lubrication oil was increased. Similarly, the results indicated that the catalytic washcoat on the filter increased soot oxidation rates on-filter, especially at low soot loading. Conversely, the ZDDP oil additive exhibited a reactivity inhibiting effect, resulting in lower soot oxidation rates. Finally, this work represents the first known in-situ soot oxidative reactivity measurements on a GPF and elucidates the effect of lubrication oil additives and catalytic washcoat on oxidation rate.