Cost-effective implementation of microalgae as a solar-to-chemical energy conversion platform requires extensive system optimization; computer modeling can bring this to bear. This work uses modified versions of the U.S. Environmental Protection Agency's (EPA's) Environmental Fluid Dynamics Code (EFDC) in conjunction with the U.S. Army Corp of Engineers' water-quality code (CE-QUAL) to simulate hydrodynamics coupled to growth kinetics of algae (Phaeodactylum tricornutum) in open-channel raceways. The model allows the flexibility to manipulate a host of variables associated with raceway-design, algal-growth, water-quality, hydrodynamic, and atmospheric conditions. The model provides realistic results wherein growth rates follow the diurnal fluctuation of solar irradiation and temperature. The greatest benefit that numerical simulation of the flow system offers is the ability to design the raceway before construction, saving considerable cost and time. Moreover, experiment operators can evaluate the impacts of various changes to system conditions (e.g., depth, temperature, flow speeds) without risking the algal biomass under study.

A machine learning framework is developed to estimate ocean-wave conditions. By supervised training of machine learning models on many thousands of iterations of a physics-based wave model, accurate representations of significant wave heights and period can be used to predict ocean conditions. A model of Monterey Bay was used as the example test site; it was forced by measured wave conditions, ocean-current nowcasts, and reported winds.These input data along with model outputs of spatially variable wave heights and characteristic period were aggregated into supervised learning training and test data sets, which were supplied to machine learning models. These machine learning models replicated wave heights with a root-mean-squared error of 9 cm and correctly identify over 90% of the characteristic periods for the test-data sets. Impressively, transforming model inputs to outputs through matrix operations requires only a fraction (< 1/1, 000 th ) of the computation time compared to forecasting with the physics-based model. There are myriad reasons why predicting wave conditions is important to the economy. Surfers aside, there are fundamental reasons why knowledge of wave conditions for the next couple of days is important. For example, shipping routes can be optimized by avoiding rough seas thereby reducing shipping times. Another industry that benefits from knowledge of wave conditions is the $160B (2014) aquaculture industry [1], which could optimize harvesting operations accordingly. Knowledge of littoral conditions is critical to military and amphibious operations by Navy and Marine Corps teams. Also, predicting the energy production from renewable energy sources is critical to maintaining a stable electrical grid because many renewable energy sources (e.g., solar, wind, tidal, wave, etc.) are intermittent. For deeper market penetration of renewable energies, combinations of increased energy storage and improved energy-generation predictions will be required. The US Department of Energy has recently invested in the design, permitting, and construction of an open-water, grid-connected national Wave Energy Test Facility at Oregon State University [2]. Given that America's technically recoverable wave-energy resource is up to 1,230 TW-hr [3], there is a strong interest in developing this renewable resource [4]. Commercializationand deployment of wave-energy technologies will require not only addressing permitting and regulatory matters, but overcoming technological challenges, one of which is being able to provide an accurate prediction of energy generation. A requirement for any forecast is that an appropriately representative model be developed, calibrated, and validated. Moreover, this model must be

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A particle tracking model is developed to simulate the transport of variably sized colloids in a fracture with a spatially variable aperture. The aperture of the fracture is treated as a lognormally distributed random variable. The spatial fluctuations of the aperture are described by an exponential autocovariance function. It is assumed that colloids can sorb onto the fracture walls but may not penetrate the rock matrix. Particle advection is governed by the local fracture velocity and diffusion by the Stokes-Einstein equation. Model simulations for various realizations of aperture fluctuations indicate that lognormal colloid size distributions exhibit greater spreading than monodisperse suspensions. Both sorption and spreading of the polydisperse colloids increase with increasing variance in the particle diameter. It is shown that the largest particles are preferentially transported through the fracture leading to early breakthrough while the smallest particles are preferentially sorbed. Increasing the variance of the aperture fluctuations leads to increased tailing for both monodisperse and variably sized colloid suspensions, while increasing the correlation length of the aperture fluctuations leads to increased spreading.

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A quasi-three-dimensional particle tracking model is developed to characterize the spatial and temporal effects of advection, molecular diffusion, Taylor dispersion, fracture wall deposition, matrix diffusion, and co-transport processes on two discrete plumes (suspended monodisperse or polydisperse colloids and dissolved contaminants) flowing through a variable aperture fracture situated in a porous medium. Contaminants travel by advection and diffusion and may sorb onto fracture walls and colloid particles, as well as diffuse into and sorb onto the surrounding porous rock matrix. A kinetic isotherm describes contaminant sorption onto colloids and sorbed contaminants assume the unique transport properties of colloids. Sorption of the contaminants that have diffused into the matrix is governed by a first-order kinetic reaction. Colloids travel by advection and diffusion and may attach onto fracture walls; however, they do not penetrate the rock matrix. A probabilistic form of the Boltzmann law describes filtration of both colloids and contaminants on fracture walls. Ensemble-averaged breakthrough curves of many fracture realizations are used to compare arrival times of colloid and contaminant plumes at the fracture outlet. Results show that the presence of colloids enhances contaminant transport (decreased residence times) while matrix diffusion and sorption onto fracture walls retard the transport of contaminants. Model simulations with the polydisperse colloids show increased effects of cotransport processes.

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Abstract. The transport of variably sized colloids (polydisperse) in a fracture with uniform aperture is investigated by a particle-tracking model that treats colloids as discrete particles with unique transport properties while accounting for either matrix diffusion or irreversible colloid deposition. For the special case of a monodisperse colloid suspension the particle-tracking model is in perfect agreement with predictions based on an existing analytical solution. It is shown that lognormal colloid size distributions exhibit greater spreading than monodisperse suspensions. Increasing the fracture porosity of the solid matrix leads to higher matrix diffusion, which in turn delays particle breakthrough for both the monodisperse and variably sized colloid suspensions. The smallest particles of a distribution are more greatly affected by matrix diffusion whereas the largest particles are transported faster and further along a fracture. Both perfect sink and kinetic colloid deposition onto fracture surfaces are examined. Kinetic deposition accounts for colloid surface exclusion by either a linear or nonlinear blocking function. For both cases the smallest colloid particles tend to preferentially deposit onto the fracture wall. Both matrix diffusion and surface deposition tend to discretize colloid distributions according to particle size so that larger particles are least retarded and smaller particles are more slowly transported. Furthermore, it is shown that the rate of colloid deposition is inversely proportional to the fracture aperture. Colloid transport differs from solute transport because of colloidal particle interactions (e.g., flocculation), mechanical clogging effects, and surface reactions (e.g., attachment). The adsorption process of colloids onto solid surfaces is often termed as deposition, attachment, or filtration. Deposition of colloids is generally affected by Brownian motion, the repulsive electric double layer, attractive van der Waals forces, and solution 707