Periodic Reporting for period 1 - SCRAMBLE (Turbulence-On-a-Chip: Supercritically Overcoming the Energy Frontier in Microfluidics)
Reporting period: 2022-04-01 to 2024-09-30
The technological opportunities enabled by understanding and controlling the microscale world have not yet been capitalized to disruptively improve energy processes, especially heat transfer and power generation. This is mainly due to the laminar flows typically encountered in microdevices resulting in low mixing and transfer rates. This is a central unsolved problem in the thermal-fluid sciences, in what some researchers refer to as “lab-on-a-chip and energy - the microfluidic frontier”. Therefore, the overarching goal of the SCRAMBLE project is to overcome this long-standing frontier by (i) discovering the fundamentals of inducing turbulent flow in microchips by means of utilizing high-pressure supercritical fluids, (ii) finding the critical conditions to drastically enhance and control mixing and transfer processes, and (iii) designing, fabricating and testing a disruptive first-ever series of turbulence-on-a-chip prototypes for transferring energy with a hundredfold performance improvement with respect to standard microsystems.
Achieving microconfined turbulence has deep scientific and engineering implications for disruptively advancing microfluidic-intensive applications, like for example in chemistry and biomedicine, and to open a new research avenue to develop and apply groundbreaking turbulent flow solutions to microfluidic energy conversion and power generation technologies (these consume an aggregated 70% of the European Union’s energy). In the medium- to long-term future, the technology proposed could enable (i) the efficient miniaturization of thermodynamic cycles for power generation, (ii) reconceptualization of the next-generation of computer processors based on remarkably powerful microfluidic-based cooling, and (iii) the adoption of novel microfluidic solutions for transportation and propulsion. These advances, together with many other potential breakthroughs, could help drive the transition toward a greener energy economy.
Achieving microconfined turbulence has deep scientific and engineering implications for disruptively advancing microfluidic-intensive applications, like for example in chemistry and biomedicine, and to open a new research avenue to develop and apply groundbreaking turbulent flow solutions to microfluidic energy conversion and power generation technologies (these consume an aggregated 70% of the European Union’s energy). In the medium- to long-term future, the technology proposed could enable (i) the efficient miniaturization of thermodynamic cycles for power generation, (ii) reconceptualization of the next-generation of computer processors based on remarkably powerful microfluidic-based cooling, and (iii) the adoption of novel microfluidic solutions for transportation and propulsion. These advances, together with many other potential breakthroughs, could help drive the transition toward a greener energy economy.
The SCRAMBLE project has already demonstrated computationally that turbulence can be achieved at microconfined conditions. In particular, it has been discovered that turbulence can be sustained at significantly lower Reynolds numbers that in traditional (low-pressure conditions) wall-bounded flows. One of the main reasons is the appearance of a baroclinic instability resulting from the interaction between pressure and density gradients as the fluid transverse the pseudo-boiling.
At present, the project is focusing on double-checking the findings experimentally. In this regard, an experiment has been designed, mounted and tested in a laboratory facility. In particular, the system uses CO2 as a working fluid (critical pressure Pc = 73.8 bar), and it is fabricated using metal, glass-glass, and silicon-Pyrex components. Moreover, the thermal part of the experiment is based on imposing a temperature difference between the top and bottom walls of the microchannel. The top wall works at a higher temperature than the critical one (Thot > Tc), and the bottom wall at a lower temperature (Tcold < Tc). In detail, (i) at the hot wall, a PI system driving a Peltier is employed to heat and control the top wall of the microchannel to the required temperature; (ii) at the cold wall, a passive system with an aluminum mass is designed and developed to maintain the temperature at room temperature. Therefore, the experiment is ready to be used for research tasks, and the project will concentrate on this task over the next months.
From a data science perspective, the project has developed a set of post-processing software tools to obtain deep insight from the computational simulations and laboratory experiments. In particular, a machine learning framework has been developed, Python scripts have been generated for processing the flow physics of wall-bounded flows presenting variable thermophysical properties, and a predictive model for confined turbulence is being developed based on dynamic mode decomposition (DMD) methods.
In terms of design-oriented validation-verification & uncertainty quantification, the project has develop a pioneering methodology to reduce the dimensionality of the problem of interest. In detail, the methodology is a data-driven augmentation of the traditional Buckingham Theorem in fluid mechanics based on active subspaces. This strategy will be very useful in the coming year to facilitate the design and optimization of the Turbulence-On-a-Chip concept.
At present, the project is focusing on double-checking the findings experimentally. In this regard, an experiment has been designed, mounted and tested in a laboratory facility. In particular, the system uses CO2 as a working fluid (critical pressure Pc = 73.8 bar), and it is fabricated using metal, glass-glass, and silicon-Pyrex components. Moreover, the thermal part of the experiment is based on imposing a temperature difference between the top and bottom walls of the microchannel. The top wall works at a higher temperature than the critical one (Thot > Tc), and the bottom wall at a lower temperature (Tcold < Tc). In detail, (i) at the hot wall, a PI system driving a Peltier is employed to heat and control the top wall of the microchannel to the required temperature; (ii) at the cold wall, a passive system with an aluminum mass is designed and developed to maintain the temperature at room temperature. Therefore, the experiment is ready to be used for research tasks, and the project will concentrate on this task over the next months.
From a data science perspective, the project has developed a set of post-processing software tools to obtain deep insight from the computational simulations and laboratory experiments. In particular, a machine learning framework has been developed, Python scripts have been generated for processing the flow physics of wall-bounded flows presenting variable thermophysical properties, and a predictive model for confined turbulence is being developed based on dynamic mode decomposition (DMD) methods.
In terms of design-oriented validation-verification & uncertainty quantification, the project has develop a pioneering methodology to reduce the dimensionality of the problem of interest. In detail, the methodology is a data-driven augmentation of the traditional Buckingham Theorem in fluid mechanics based on active subspaces. This strategy will be very useful in the coming year to facilitate the design and optimization of the Turbulence-On-a-Chip concept.
The project has already developed a series of novel methodologies, interdisciplinary technologies and participated in dissemination and knowledge transfer activities:
- An open-source massively-parallel accelerated flow solver has been generated and made publicly available to the scientific community. The solver is available online at:
https://gitlab.com/ProjectRHEA/flowsolverrhea(opens in new window).
- A novel kinetic-energy- and pressure-equilibrium-preserving numerical scheme for real-gas turbulent flows has been developed and tested.
- Extension of the artificial compressibility method (ACM) to real-gas turbulent flows has been achieved.
- A thermodynamics-informed neural network methodology to recover thermophysical information of high-pressure transcritical fluids from velocity data has been developed and tested.
- The computational demonstration that turbulence can be achieved at microconfined conditions by means of high-pressure transcritical fluids is a significant scientific achievement beyond the
current state-of-the-art. This achievement was planned as is one of the main objectives indicated in the project proposal.
- An open-source massively-parallel accelerated flow solver has been generated and made publicly available to the scientific community. The solver is available online at:
https://gitlab.com/ProjectRHEA/flowsolverrhea(opens in new window).
- A novel kinetic-energy- and pressure-equilibrium-preserving numerical scheme for real-gas turbulent flows has been developed and tested.
- Extension of the artificial compressibility method (ACM) to real-gas turbulent flows has been achieved.
- A thermodynamics-informed neural network methodology to recover thermophysical information of high-pressure transcritical fluids from velocity data has been developed and tested.
- The computational demonstration that turbulence can be achieved at microconfined conditions by means of high-pressure transcritical fluids is a significant scientific achievement beyond the
current state-of-the-art. This achievement was planned as is one of the main objectives indicated in the project proposal.