Engineering innovations—including those in heat and mass transfer—are needed to provide food, water, and power to a growing population (i.e., projected to be 9.8 × 109 by 2050) with limited resources. The interweaving of these resources is embodied in the food, energy, and water (FEW) nexus. This review paper focuses on heat and mass transfer applications which involve at least two aspects of the FEW nexus. Energy and water topics include energy extraction of natural gas hydrates and shale gas; power production (e.g., nuclear and solar); power plant cooling (e.g., wet, dry, and hybrid cooling); water desalination and purification; and building energy/water use, including heating, ventilation, air conditioning, and refrigeration technology. Subsequently, this review considers agricultural thermal fluids applications, such as the food and water nexus (e.g., evapotranspiration and evaporation) and the FEW nexus (e.g., greenhouses and food storage, including granaries and freezing/drying). As part of this review, over 100 review papers on thermal and fluid topics relevant to the FEW nexus were tabulated and over 350 research journal articles were discussed. Each section discusses previous research and highlights future opportunities regarding heat and mass transfer research. Several cross-cutting themes emerged from the literature and represent future directions for thermal fluids research: the need for fundamental, thermal fluids knowledge; scaling up from the laboratory to large-scale, integrated systems; increasing economic viability; and increasing efficiency when utilizing resources, especially using waste products.

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Non-equilibrium statistical mechanics models can be used to construct reduced order models from the time-dynamics data such as numerical or physical fluid mechanics experiments. One of the well-established statistical projection methods is the Kramers-Moyal expansion (KM) method. The first two terms of the KM expansion result can be used to construct a non-linear Langevin equation, which can serve as the statistically-trained reduced-order model. This non-linear Langevin equation can be approximated to the Fokker-Planck equation, which is similar to Advection-Diffusion equation, thereby preserving some characteristics of fluctuations associated with fluid mechanics. The KM method captures continuous-time dynamics, however, any data obtained through measurement is discrete. In order to accurately capture the time dynamics of the discrete data, the method for calculating the KM coefficients must be carefully chosen and implemented. To better represent the solution from discrete data, the drift and diffusion coefficients can be calculated at multiple time scales and then extrapolated to a time scale of zero, assuming a linear correlation. One challenge in using this method is that the calculated KM coefficients are only accurate for time scales greater than the Taylor microscale. This means that the extrapolation must use only the KM coefficients calculated for time scales greater than the Taylor microscale, however, this value is not always provided from the data nor simple to calculate. This work presents a method of approximating the Taylor microscale from the data through the relationship between the Markov property and the Taylor microscale and implementing this method to find the extrapolated KM coefficients. The KM method implementing the Taylor microscale estimation was applied to existing DNS turbulent channel flow data to model a time series. This generated time series was then compared to the DNS data using a statistical analysis including probability density function, autocorrelation, and power spectral density.

Recent studies have shown that the presence of dissolved salts in water can exhibit peculiar flow boiling and two-phase flow regimes. Two-phase flow and convective flow boiling are typically characterized with the help of void fraction measurements. To quantitatively improve our understanding of two-phase flow and boiling phenomenon with seawater coolant, void fraction data are needed, which can not be obtained from optical imaging. In this paper, we present experimental void fraction measurements of saturated flow boiling of tap water and seawater using X-ray radiography. X-rays with a maximum energy level of 40 KeV were used for imaging the exit region of the heated test section. At lower heat flux levels, the two phase flow in seawater was bubbly and homogeneous in nature, resulting in higher void fractions as compared to tap water. With an increase in heat flux, the flow regime was similar to slug flow, and void fraction measurements approached similarity with tap water. The predicted pressure drop using the measured void faction shows good agreement with the measured total pressure drop across the test section, demonstrating the validity of the measurement process.

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Thermal hydraulics, in certain components of nuclear reactor systems, involve complex flow scenarios, such as flows assisted by free jets and stratified flows leading to turbulent mixing and thermal fluctuations. These complex flow patterns and thermal fluctuations can be extremely critical from a reactor safety standpoint. The component-level lumped approximations (0D) or one-dimensional approximations (1D) models for such components and subsystems in safety analysis codes cannot capture the physics accurately, and may introduce a large degree of modeling uncertainty. On the other hand, high-fidelity computational fluid dynamics codes, which provide numerical solutions to the Navier–Stokes equations, are accurate but computationally intensive, and thus cannot be used for system-wide analysis. An alternate way to improve reactor safety analysis is by building reduced-order emulators from computational fluid dynamics (CFD) codes to improve system scale models. One of the key challenges in developing a reduced-order emulator is to preserve turbulent mixing and thermal fluctuations across different-length scales or time-scales. This paper presents the development of a reduced-order, non-linear, “Markovian” statistical surrogate for turbulent mixing and scalar transport. The method and its implementation are demonstrated on a canonical problem of differentially heated channel flow, and high-resolution direct numerical simulations (DNS) data are used for emulator or surrogate development. This statistical surrogate model relies on Kramers–Moyal expansion and emulates the turbulent velocity signal with a high degree of accuracy.