Hierarchical compact models for simulation of electronic chip packages

In gas turbine engineering, film cooling is critical for protecting engine components from high temperatures that can compromise safety and performance. However, the process is fraught with uncertainties due to complex inflow conditions and geometrical configurations, significantly affecting cooling efficiency, and it’s important to pinpoint the dominant factors in a quantified way. Traditional methods, such as the Monte Carlo (MC) approach, struggle with the ‘curse of dimensionality’, rendering them computationally intensive as variable counts increase. This study addresses these challenges by implementing a deep learning strategy using a Convolutional Neural Network (CNN) model to efficiently predict film cooling effectiveness, reducing computational loads compared to traditional Computational Fluid Dynamics (CFD) simulations, which can surrogate the CFD model and also provide the gradient information utilizing by subsequent works. Additionally, the study employs Active Subspace (AS) analysis for dimensionality reduction, identifying dominant parameters based on the trained CNN model. This approach not only enhances the speed of simulations, but also provide an effective way to analyse dominant parameters and carry out optimizations. Results show a strong correlation between the CNN predictions and detailed CFD simulations, validating the model’s efficacy in capturing complex cooling dynamics. And the dominant analysis based on AS method gives the most important factors: blowing ratio, mainstream Mach number. Subsequent optimizations are done by an inverse network which treated with low-dimensional active inputs, and the results show an improvement compared to the maximum results in all samples.

This study entails an optimal airfoil-based curve section attached to a straight blade segment, forming a hybrid-bladed Savonius hydrokinetic turbine (HSHT) design. The aim is to investigate its detailed hydrodynamic performance under various design and flow conditions, common in low discharge channels. Three turbine types are conceptualized with different arc radii and aspect ratios. Initially, transient computational fluid dynamics (CFD) simulations are performed to investigate torque variation and compare their flow physics, which is followed by Taguchi optimization. Further, detailed CFD simulations have been carried out. The Taguchi optimized turbine design showcases five blade-fluid flow control-based interactions—(i) advancing blade's arc curvature flow control (both pressure and suction sides), (ii) overlapping and side gap flow control, (iii) curve edge gap flow control, (iv) Coanda-vector like flow control, and (v) vortical flow control, which make the optimal turbine design a better choice for pico-scale power generation. When the optimal curve profile (15 mm arc radius) and optimal aspect ratio (0.51) are used with water flow stream-speed 0.5 m/s, it exhibits a maximum Cp of 0.272 and CT of 0.68, respectively, for tip-speed ratio 0.7. Blade overlapping 20% and thickness 2 mm further enhance Cp and CT by 2.20% and 3.23%, respectively.

Thermal hydraulic analysis of the AFIP-7 irradiation test in the Advanced test Reactor – Model correlation and performance evaluation

Optimization methods have been widely applied to the aerodynamic design of gas turbine blades. While applying optimization to high-fidelity computational fluid dynamics (CFD) simulations has proven capable of improving engineering design performance, a challenge has been overcoming the prolonged run-time due to the computationally expensive CFD runs. Reduced-order models and, more recently, machine learning methods have been increasingly used in gas turbine studies to predict performance metrics and operational characteristics, model turbulence, and optimize designs. The application of machine learning methods allows for utilizing existing knowledge and datasets from different sources, such as previous experiments, CFD, low-fidelity simulations, 1D or system-level studies. The present study investigates inserting a machine learning model that utilizes such data into a high-fidelity CFD driven optimization process, and hence effectively reduces the number of required evaluations of the CFD model. Artificial Neural Network (ANN) models were trained on data from over three thousand two-dimensional (2D) CFD analyses of turbine blade cross-sections. The trained ANN models were then used as surrogates in a nested optimization process alongside a full three-dimensional Navier–Stokes CFD simulation. The much lower evaluation cost of the ANN model allows for tens of thousands of design evaluations to guide the search of the best blade profiles to be used in the more expensive, high-fidelity CFD runs, improving the progress of the optimization while reducing the required computation time. It is estimated that the current workflow achieves a five-fold reduction in computational time in comparison to an optimization process that is based on three-dimensional (3D) CFD simulations alone. The methodology is demonstrated on the NASA/General Electric Energy Efficient Engine (E3) high pressure turbine blade and found Pareto front designs with improved blade efficiency and power over the baseline. Quantitative analysis of the optimization data reveals that some design parameters in the present study are more influential than others, such as the lean angle and tip scaling factor. Examining the optimized designs also provides insight into the physics, showing that the optimized designs have a lower amount of pressure drop near the trailing edge, but have an earlier onset of pressure drop on the suction side surface when compared to the baseline design, contributing to the observed improvements in efficiency and power.

This paper presents a novel heating, ventilation, and air-conditioning system that can achieve significant energy savings. A smart register that can reduce overall energy costs by up to 30%, compared to conventional devices, is demonstrated. A high abstraction model based on a computational fluid dynamics (CFD) simulation is developed to predict the diffused airflow from the register. The model is validated to within a 10% error, and is found to be 1000 times more efficient than the detailed CFD simulations. Finally, a prototype of the electroactive smart air-conditioning vent register is implemented, and adjustments of the register’s tunnels are implemented.

While designing the power requirements of a ship, the most important factor to be considered is the ship resistance, or the sea drag forces acting on the ship. It is important to have an estimate of the ship resistance while designing the propulsion system since the power required to overcome the sea drag forces contribute to ‘losses’ in the propulsion system. There are three main methods to calculate ship resistance: Statistical methods like the Holtrop-Mennen (HM) method, numerical analysis or CFD (Computational Fluid Dynamics) simulations, and model testing, i.e. scaled model tests in towing tanks. At the start of the design stage, when only basic ship parameters are available, only statistical models like the HM method can be used. Numerical analysis/ CFD simulations and model tests can be performed only when the complete 3D design of the ship is completed. The present paper aims at predicting the calm water ship resistance using CFD simulations, using the Flow-3D software package. A case study of a roll-on/roll-off passenger (RoPax) ferry was investigated. Ship resistance was calculated at various ship speeds. Since the mesh affects the results in any CFD simulation, multiple meshes were used to check the mesh sensitivity. The results from the simulations were compared with the estimate from the HM method. The results from simulations agreed well with the HM method for low ship speeds. The difference in the results was considerably high compared to the HM method for higher ship speeds. The capability of Flow-3D to perform ship resistance analysis was demonstrated.

Bench test and computational fluid dynamics (CFD ) simulation of the air pump loss on the 380 hydraulic retarder are completed in this paper . It turns out that the CFD simulation has nice reliability from the contrast of the air pump loss between the test and the simulation . And on this foundation , CFD simulation of two kinds of retarders with different turbulent structures is done . The contrast of the results of the simulation give the effects of different turbulent structures on cutting down the are pump loss .

Recently, image-based computational fluid dynamic (CFD) simulations have been proposed to investigate the local hemodynamics inside human cerebral aneurysms. It was suggested that the knowledge of the computed three-dimensional flow fields can be used to assist clinical risk assessment and treatment decision making. Therefore, it was desired to know the reliability of CFD for cerebral blood flow simulation, and be able to provide clinical feedback. However, the validations were not yet comprehensive as they lack either patient-specific boundary conditions (BCs) required for CFD simulations or quantitative comparison methods. In this study, based on a recently proposed in-vitro quantitative CFD evaluation approach via virtual angiography, the CFD evaluation was extended from phantom to patient studies. In contrast to previous work, patient-specific blood flow rates obtained by transcranial color coded Doppler ultrasound measurements were used to impose CFD BCs. Virtual angiograms (VAs) were constructed which resemble clinically acquired angiograms (AAs). Quantitative measures were defined to thoroughly evaluate the correspondence of the detailed flow features between the AAs and the VAs, and thus, the reliability of CFD simulations. The proposed simulation pipeline provided a comprehensive validation method of CFD simulation for reproducing cerebral blood flow, with a focus on the aneurysm region. Six patient cases were tested and close similarities were found in terms of spatial and temporal variations of contrast agent (CA) distribution between AAs and VAs. For patient #1 to #5, discrepancies of less than 11% were found for the relative root mean square errors in time intensity curve comparisons from characteristic vasculature positions. For patient #6, where the CA concentration curve at vessel inlet cannot be directly extracted from the AAs and given as a BC, deviations about 20% were found. As a conclusion, the reliability of the CFD simulations was well confirmed. Besides, it was shown that the accuracy of CFD simulations was closely related to the input BCs.

The chemical composition and properties of fuels used in compression-ignition engines can influence engine performance significantly. Consequently, the modeling of fuel chemistry within computational fluid dynamics (CFD) simulations of diesel and other compression ignition engines is important. Modern detailed chemical mechanisms may provide predictive modeling of fuel chemistry; however, they are generally far too computationally expensive for use in CFD. We present simulations of diesel engine combustion, focusing on the prediction of ignition, using the CONVERGE CFD software package. A CFD simulation framework with models for turbulence and spray breakup and atomization is presented with a reduced global reaction model to describe fuel oxidation and ignition. The global reaction model incorporates a single parameter, the derived cetane number (DCN), to describe fuel reactivity variability. CFD simulations are compared to experiments carried out in a single-cylinder diesel engine for compositionally diverse conventional and alternative diesel and jet fuels. Model-experiment comparisons show general agreement for ignition timing and the influence of fuel variability on ignition timing. In addition, the sensitivity of CFD predictions on the chemistry, turbulence, and spray models is illustrated.

Today, many wells in heterogeneous carbonate reservoirs throughout the Middle East and Caspian Sea area are being completed with long intervals and complex completion equipment, such as limited entry liners and inflow control devices (ICDs). Accurate modeling and understanding of fluid placement during a stimulation operation through such long complex completions can be quite challenging. In this paper, an integrated approach of modeling, experiments and computational fluid dynamics (CFD) simulations will be presented. A computational tool based on a transient one-dimensional (1D) approach has been developed in-house to model stimulation through such completions. In order to validate the 1D approach for reliable fluid displacement predictions, annulus fluid displacement experiments in perforated liners supported by CFD simulations have been conducted. The experimental setup consists of an outer Lucite pipe representing the well-bore geometry and an inner aluminum pipe with multiple perforations of variable diameter. The setup mimics the real field application in a 1:1 scale with respect to the pipe and perforation diameters while the test section is limited to 27 ft. The experimental procedure is as follows: fluid is pumped into the annulus through the perforations until the annulus is completely filled,the fluid tank is switched "on-the-fly" to the tank with the displacing fluid resulting in the displacement of the annulus fluid by the displacing fluid through the perforations. The experiments and the CFD simulations indicate that even with a single open perforation inside the 27-ft test section, the three-dimensional (3D) flow field around the perforation rapidly transitions to uniform annular flow within a few feet uniformly displacing the annulus fluid. The model also provides insight into various well injection processes in sandstone and carbonate formation, such as scale squeezes, solvent treatments, and HF acid treatments. Introduction The length of production and injection intervals in a single well continues to increase in order to contact more reservoir rock with a single wellbore penetration and improve project economics. Multiple, thick stacked reservoirs are accessed by vertical and deviated wells while thinner reservoirs are being exploited with longer and longer horizontal wells1–3. To keep up with drilling capability, completion technology has advanced considerably in the past few years. Horizontal wells are "compartmentalized" using mechanical and/or swellable packers. Within a given compartment, injection and production can be controlled by use of inflow control devices (ICDs), limited entry (LE) liners, and/or sliding sleeves. The liners also make it easier to enter the completion interval with logging tools, wire line, and coiled tubing. While various tools are available to evaluate long-term, steady-state production and injection into such wells, modeling of short-term, transient injection into such completions is very challenging due to the long lateral lengths, limited entry points from the liner to each compartment, and the presence of an open annulus with occasional packer isolations. This is especially important for long wells in carbonate formations as all wells require acid stimulation upon initial completion, and occasionally throughout the well life.

A normally-open thermally-actuated microvalve was designed (using microfabrication/soft-lithography techniques involving 3D Printed molds), assembled and tested. The motivation of the research work is to develop an array of microvalves for precise delivery of water to individual plants in a field (with the goal of developing smart irrigation systems for high value cash-crops in the agricultural sector). It is currently impossible to control application of irrigation-water at the level of a single plant. If such a capability were practically available on farms, the result would be a step change in precision agriculture, such that the output of every plant in a farm field could be optimized (i.e., food-water-energy nexus in sustainability applications). The aim of this study is to develop and test a microfluidic system (consisting of a microvalve array) that could be controlled, capillary by capillary, to deliver the needed amount of water to individual plants in a large field. Two types of test fluids were leveraged for thermo-hydraulic actuation of the microvalves developed in this study: (a) Design-I: using air, and (b) Design-II: using Phase Change Material (PCM). The PCM used in this study is PureTemp29. The proposed approach enabled a simple and cheap design for microvalves that can be manufactured easily and are robust to weather conditions (e.g., when exposed to the elements in orchards and open fields). Other advantages include: safe and reliable operation; low power consumption; can tolerate anomalous pressure loads/fluctuations; simple actuation; affords easy control schemes; is amenable for remote control; provides long-term reliability (life-cycle duration estimated to be 3∼5 years); can be mass produced and is low maintenance (possibly requiring no maintenance over the life time of operation). The microvalve consists of two layers: a flow layer and a control layer. The control layer is heated from below and contains a microfluidic chamber with a flexible polymeric thin-membrane (200 microns in thickness) on top. The device is microfabricated from Poly-Di-Methyl-Siloxane (PDMS) using soft lithography techniques (using a 3D Printed mold). The control chamber contains either air (thermo-pneumatic actuation) or PCM (thermo-hydraulic actuation involving repeated melting/freezing of PCM). The flow layer contains the flow channel (inlet and outlet ports, horizontal section and valve seat). The experimental results from testing the efficacy of the two types of micro-valves show a 60% reduction (for thermo-pneumatic actuation using air) and 40% reduction (for thermo-hydraulic actuation using PCM) in water flow rates for similar actuation conditions (i.e., heater temperature values). PCM design is expected to consume less power (lower OPEX) for long-term actuation but may have slower actuation speed and have higher manufacturing costs (CAPEX). Air actuation design is expected to consume more power (higher OPEX) for longer-term operation but may have faster actuation speeds and lower manufacturing costs (CAPEX). Computational Fluid Dynamics (CFD) simulations were performed to investigate the effect of flowing water (in the microfluidic channel) on the average absolute pressure and temperature of air in the actuation chamber. The CFD simulations were performed using a commercial tool (Ansys™ 2019R1®). The results from the CFD simulations are presented in this study.

In response to a solicitation for Computational Fluid Dynamic (CFD) simulations of flow in a T-junction from the Nuclear Energy Agency of the Organization for Economic Co-operation and Development (OECD/NEA) [1], Structural Integrity (SI) Associates of San Jose, CA, USA has completed a simulation using the commercially available CFD code ANSYS-CFX. The simulation was carried out for geometry and flow conditions matching an experiment underway at Vattenfall Research and Development in Sweden where two streams of differing temperature mix in a T-junction. The benchmark was to be a blind test of CFD simulations. Experimental results were not released until after benchmark participants submitted the results of their simulation to the OECD/NEA. The CFD simulation done by SI was carried out on an all hexahedral mesh of 1,881,504 elements generated using the ANSYS Mechanical Finite Element program. The mesh was then transferred into the CFX program and, after applying proper boundary conditions, a time-dependent simulation was done using the Large Scale Eddy Simulation (LES) turbulence model [2] with Wall Adjusting Local Eddy Viscosity (WALE) sub-grid scale (SGS) turbulence model [2, 3]. Comparisons of the CFD results to the Vattenfall data show close correspondence for mean and root-mean-square (rms) turbulent temperature fluctuations.

CFD simulation and experimental validation of a GM type double inlet pulse tube refrigerator

Computational fluid dynamic (CFD) simulations in support of the SHARP-B2 (Slender Hypervelocity Aerothermodynamic Research Probes) program are presented. Pre-flight simulations of the aerothermal flight environment have been made to aid in the design of the flight hardware and instrumentation. Simulations of a critical flight qualification ground test performed in the 20 MW Panel Test Facility (PTF) arc jet, which is equipped with a semi-elliptical nozzle, have also been performed. The arc jet simulations were performed with the same CFD code that is used in the flight calculations. For both the flight and the arc jet simulations, the CFD results are used to provide surface heat transfer coefficients that serve as boundary conditions for an in-depth conduction thermal response code. By using the same CFD tool to compute the aerothermal environments for both the ground test and the flight test environment, useful comparisons are made regarding the traceability of the ground test environment to flight. Introduction The NASA Ames Research Center has been developing new Ultra High Temperature Ceramics (UHTC's) with the goal that these materials will enable sharp leading edges to be used for future space vehicles. Because of their unique thermo-structural properties, these materials are capable of operation at temperatures near 5100 °F without ablation. The materials have been developed and tested in ground-based arc jet facilities, and a flight test program designated SHARP (Slender Hypervelocity Aerothermodynamic Research Probes) has been initiated. The first flight demonstration, SHARP-B1, incorporated a UHTC nosetip mounted on a U.S. Air Force Mkl2A entry vehicle in collaboration with Sandia National Laboratory (SNL), and was successfully flown in May, 1997. The second flight test, SHARP-B2, which incorporated four instrumented UHTC strakes mounted on the side of the entry vehicle, was flown in September 2000. The goal of these flight tests is to assess the performance of the materials under entry conditions. The use of Computational Fluid Dynamic (CFD) simulations for the design of Thermal Protection Systems (TPS) has evolved to an extent that they have been integrated into the preliminary design process of Reusable Launch Vehicle (RLV) programs such as the X-33. Real-gas CFD simulations with finite-rate chemistry and proper boundary conditions for coupling with material thermal response codes are now routinely performed on high-end laboratory workstations. Much of the ground based testing of advanced TPS components is done in arc-heated test facilities such as those located in the Arc Jet Complex at the NASA Ames Research Center. These facilities are capable of simulating the high enthalpy flow environment experienced by entry vehicles for a broad range of test conditions and configurations. Semi-elliptical nozzles are available for the testing of flat panels in a high enthalpy boundary layer environment. CFD is • Aerospace Engineer, Reacting Flow Environments Branch, Member AIAA. t Senior Research Scientist, ELORET, Member AIAA. Copyright © 2001 by the American Institute of Aeronautics and Astronautics, Inc. No copyright is asserted in the United States under Title 17, U.S. Code. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental Purposes. All other rights are reserved by the copyright owner. (c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.

In this review, the application of computational fluid dynamics (CFD) simulations in analyzing thermal processes within food technology is explored. The focus is on understanding heat transfer, fluid flow, and temperature distribution during various food processing methods, such as baking, frying, pasteurization, and cooling. Detailed insights that are often challenging to obtain through experimental methods alone are provided by CFD simulations, allowing for the optimization of process parameters to enhance product quality and safety. It is demonstrated that CFD can effectively model complex thermal phenomena, providing valuable data on temperature gradients and flow patterns. These simulations assist in the designing of more efficient processing equipment, improving energy consumption, and ensuring uniform heat treatment, which is crucial for maintaining the nutritional and sensory attributes of food products. Furthermore, the integration of CFD in the food industry leads to significant advancements in product development, reducing the time and cost associated with experimental trials. Future research should focus on refining these models for greater accuracy and exploring their application in emerging food processing technologies.