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Miljkovic, Nenad

Publications and source records attributed to Miljkovic, Nenad.

25 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Gas-Phase Temperature Mapping of Evaporating Microdroplets

Evaporation is a ubiquitous and complex phenomenon of importance to many natural and industrial systems. Evaporation occurs when molecules near the free interface overcome intermolecular attractions with the bulk liquid. As molecules escape the liquid phase, heat is removed, causing evaporative cooling. The influence of evaporative cooling on inducing a temperature difference with the surrounding atmosphere as well as within the liquid is poorly understood. Here, we develop a technique to overcome past difficulties encountered during the study of heterogeneous droplet evaporation by coupling a piezo-driven droplet generation mechanism to a controlled micro-thermocouple to probe microdroplet evaporation. The technique allowed us to probe the gas-phase temperature distribution using a micro-thermocouple (50 μm) in the vicinity of the liquid–vapor interface with high spatial (±10 μm) and temporal (±100 ms) resolution. We experimentally map the temperature gradient formed surrounding sessile water droplets having varying curvature dictated by the apparent advancing contact angle (100° ≲ θ ≲ 165°). The experiments were carried out at temperatures below and above ambient for a range of fixed droplet radii (130 μm ≲ R ≲ 330 μm). Our results provide a primary validation of the centuries-old theoretical framework underpinning heterogeneous droplet evaporation mediated by the working fluid, substrate, and gas thermophysical properties, droplet apparent contact angle, and droplet size. We show that microscale droplets residing on low-thermal-conductivity substrates such as glass absorb up to 8× more heat from the surrounding gas compared to droplets residing on high-thermal-conductivity substrates such as copper. Finally, our work not only develops an experimental understanding of the heat transfer mechanisms governing droplet evaporation but also presents a powerful platform for the study and characterization of liquid–vapor transport at curved interfaces wetting and nonwetting advanced functional surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Review of heat transfer enhancement techniques for single phase flows

The thermal energy exchange between a flowing fluid and its confining channel is a ubiquitous process in modern society. To enhance the fluid-to-wall or wall-to-fluid heat transfer, several techniques have been developed to maximize the contact area between the fluid and the inner wall and/or disrupt the flow to enhance circulation or induce turbulence. Deployment of channels having features capable of enhancing heat transfer enables the reduction of heat exchanger size while maintaining performance. Reduction in equipment size is critical due to the ability to minimize the required volume of costly working fluids and to mitigate potential safety concerns associated with total system fluid volume. Here, a comprehensive review of single-phase heat transfer enhancement techniques is presented. The article provides a thorough comparison by analyzing the heat transfer rate, pressure drop, and other operational aspects. Single-phase heat transfer enhancement methods are divided into active and passive techniques. Active methods such as electrohydrodynamic (EHD), magnetohydrodynamics (MHD), or mechanical motion require external power to create enhancement. Passive methods such as dimples, fins, or tape inserts do not require external input and rely only on surface modification. Although active methods are more expensive and difficult to implement compared to passive techniques, it enables active control of heat transfer augmentation. Finally, this review develops and summarizes key learning data for design optimization enabled by additive manufacturing and machine learning algorithms, helping to inform these next-generation heat exchanger design methodologies for a plethora of modern applications such as electrification of vehicles, computing, and classical industries.

42 ENGINEERING↗
DOE OSTI2020

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗
DOE OSTI2020

Dissolvable Template Nanoimprint Lithography: A Facile and Versatile Nanoscale Replication Technique

Nanoimprinting lithography (NIL) is a next-generation nanofabrication method, capable of replicating nanostructures from original master surfaces. In this work, we develop highly scalable, simple, and nondestructive NIL using a dissolvable template. Termed dissolvable template nanoimprinting lithography (DT-NIL), our method utilizes an economic thermoplastic resin to fabricate nanoimprinting templates, which can be easily dissolved in simple organic solvents. Additionally, we used the DT-NIL method to replicate cicada wings which have surface nanofeatures of ~100 nm in height. The master, template, and replica surfaces showed a >~94% similarity based on the measured diameter and height of the nanofeatures. The versatility of DT-NIL was also demonstrated with the replication of re-entrant, multiscale, and hierarchical features on fly wings, as well as hard silicon wafer-based artificial nanostructures. The DT-NIL method can be performed under ambient conditions with inexpensive materials and equipment. Our work opens the door to opportunities for economical and high-throughput nanofabrication processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Fouling modeling and prediction approach for heat exchangers using deep learning

In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. Here, the efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10 –4 KW –1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R 2 ), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.

42 ENGINEERING↗
DOE OSTI2020

Composite Structured Surfaces for Durable Dropwise Condensation

Dropwise condensation of steam on hydrophobic substrates has a 10X higher heat transfer coefficient compared to filmwise condensation. To promote dropwise condensation, low surface energy hydrophobic coatings (polymers) are typically utilized. The low intrinsic thermal conductivity (k < 1 W/(m · K)) of polymers, coupled with high heat transfer coefficient of dropwise condensation (100 kW/(m 2 · K)), necessitates that the coating be thin (< 1µm) in order to avoid reducing the overall heat exchanger conductance. However, thin polymeric films easily degrade. The two opposing requirements result in the need for optimization between the durability (thick coating) and the heat transfer (thin coating). To enable high thermal conductivity in thicker coatings, we develop metal-polymer structured surfaces. By using porous structures as inter-connected heat-conducting backbones that are filled with hydrophobic materials, we enable tuning of the coating effective thermal conductivity and surface energy. Three metal structures were studied; micro/nanowires, inverse opals, and sintered spheres. Heat transfer performance was calculated using three-dimensional finite element method simulations with two distinct boundary conditions; convection at the walls and isothermal walls. Interestingly, the overall conductance shows up to 40% difference depending on the boundary condition used in calculating the composite coating effective thermal conductivity. We use our model to predict the heat transfer performance as a function of metal fraction by volume and by surface area for condensation. By coupling our thermal simulations with a previously verified analytical model for predicting wetting behavior on heterogeneous surfaces, we propose a regime map to predict dropwise-to-filmwise transition. Furthermore, our work not only forms a starting point for the development of durable dropwise condensing surfaces, it identifies important considerations needed for computing effective thermal conductivity of composites.

42 ENGINEERING↗