System and method for simulation and design of discrete droplet microfluidic systems

Patent metadata

Field	Value
Publication number	US-9779189-B2
Application number	US-201414905568-A
Country	US
Kind code	B2
Filing date	Jul 11, 2014
Priority date	Jul 17, 2013
Publication date	Oct 3, 2017
Grant date	Oct 3, 2017

Abstract

A computerized method for designing a discrete droplet microfluidic system: (a) provides an initial set of droplet based networks; (b) codes each droplet based network into a data structure such that all the data structures form a current set of data structures; (c) creates new data structures by performing one or more genetic operators on the current set of data structures; (d) adds new data structures to the current set of data structures; (e) creates a new set of data structures that satisfies one or more design parameters; (f) replaces the current set of data structures with the new set of data structures; (g) repeats steps (c), (d), (e) and (f) until the new set of data structures has been created a third number of times; and (h) displays/outputs the current set of data structures as possible designs for the discrete droplet microfluidic system to one or more output devices.

First claim

The invention claimed is:

1. A computerized method for designing a discrete droplet microfluidic system comprising the steps of:
(a) providing an initial set of droplet based networks containing a first number of droplet based networks, wherein each droplet based network comprises one or more inlets, one or more outlets, two or more channels connected between the one or more inlets and the one or more outlets, and one or more bypass channels connected between the two or more channels;
(b) coding each droplet based network into a data structure such that all the data structures form a current set of data structures;
(c) creating one or more new data structures by performing one or more genetic operators on one or more selected data structures from the current set of data structures;
(d) adding the one or more new data structures to the current set of data structures;
(e) creating a new set of data structures that satisfies one or more design parameters for the discrete droplet microfluidic system and contains at least a second number of data structures selected from the current set of data structures, wherein the second number is less than a total number of data structures within the current set of data structures, wherein a first group of the new data structures are selected using a fitness score, a second group of the new data structures are selected using a combination of the fitness score and a diversity score, and a third group of the new data structures are selected randomly, and wherein the first group of the new data structures contains approximately 10% of the second number of data structures, the second group of the new data structures contains approximately 80% of the second number of data structures, and the third group of the new data structures contains approximately 10% of the second number of data structures;
(f) replacing the current set of data structures with the new set of data structures;
(g) repeating steps (c), (d), (e) and (f) until the new set of data structures has been created a third number of times;
(h) displaying or outputting the current set of data structures as designs for the discrete droplet microfluidic system to one or more output devices;
wherein steps (c), (d), (e), (f), (g) and (h) are executed by one or more processors communicably coupled to the one or more output devices; and
fabricating the discrete droplet microfluidic system based on the displayed or outputted designs using a three-dimensional printer.

2. The method as recited in claim 1 , further comprising the step of providing the one or more design parameters for the discrete droplet microfluidic system, wherein the one or more design parameters comprise one or more structural properties, one or more fluid properties or a combination thereof.

3. The method as recited in claim 1 , wherein the step of providing the initial set of droplet based networks comprises the step of randomly generating the initial set of droplet based networks.

4. The method as recited in claim 1 , wherein:
the one or more bypasses comprise a backward bypass, a vertical bypass, a forward bypass or a combination thereof; or
each data structure comprises a total number of bypasses present in the droplet based network, and a position of the bypasses present in the droplet based network.

5. The method as recited in claim 1 , wherein the one or more genetic operators comprise a mutation, a one point crossover or a combination thereof.

6. The method as recited in claim 5 , wherein:
the mutation genetic operator creates a new data structure by the step of either (i) adding two new bypass channels to one of the selected data structures, or (ii) adding a new bypass channel to one of the selected data structures or removing an existing bypass channel from one of the selected data structures; and
the one point crossover genetic operator combines two of the selected data structures to create two new data structures.

7. The method as recited in claim 5 , wherein the one point crossover genetic operator:
randomly selects a first data structure (TB 1 ) having a first portion (TB 1 (1:ζ−1)) and a second portion (TB 1 (ζ:p)) and a second data structure (TB 2 ) having a first portion (TB 2 (1:ζ−1)) and a second portion (TB 2 (ζ:p)) from the current set of data structures, where ζ is a random integer generated from 1 to length of the TB group (p);
creates a first new data structure (TB′) and a second new data structure (TB″) as follows:
TB′=[TB 1 (1:ζ−1) TB 2 (ζ: p )]

TB″=[TB 2 (1:ζ−1) TB 1 (ζ: p )]; and

generates a first real value (V′) for the first new data structure (TB′) and a second real value (V′) for the second new data structure (TB′) as follows:
V′=γV 1 +(1−γ) V 2

V ″=(1−γ) V 1 +γV 2

where: V i is a first real value for the first data structure,
V 2 is a second real value for the second data structure, and
γ is a random number between 0 and 1.

8. The method as recited in claim 5 , wherein the mutation genetic operator is used whenever a first probability is calculated to be less than or equal to a first specified probability, and the one point crossover genetic operator is used whenever the first probability is calculated to be greater than the first specified probability.

9. The method as recited in claim 8 , wherein the mutation genetic operator further comprises a first tier mutation genetic operator and a second tier mutation genetic operator such that the first tier mutation genetic operator is used whenever a second probability is calculated to be less than or equal to a second specified probability and the second tier mutation genetic operator is used whenever the second probability is calculated to be greater than the second specified probability.

10. The method as recited in claim 1 , further comprising the step of repeating steps (c) and (d) until the current set of data structures contains at least a fourth number of data structures.

11. The method as recited in claim 10 , wherein the fourth number of data structures is approximately equal to two times the first number of droplet based networks in the initial set of droplet based networks.

12. The method as recited in claim 1 , wherein the second number of data structures is approximately equal to the first number of droplet based networks in the initial set of droplet based networks.

13. The method as recited in claim 1 , further comprising the step of increasing the third group of the new data structures and decreasing the second group of new data structures whenever the new data structures are less than 80% diverse.

14. The method as recited in claim 1 , wherein the fitness score is calculated using

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Assignees

• Univ Texas Tech System

Inventors

• Maddala Jeevan
• Rengaswamy Raghunathan

Classifications

• G06N3/086

  using evolutionary algorithms, e.g. genetic algorithms or genetic programming · CPC title

• G06F16/212

  with details for data modelling support · CPC title

• G06F30/18

  Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling (circuit design at the physical level G06F30/39; network planning tools for wireless communication networks H04W16/18) · CPC title

• G06F2111/10

  Numerical modelling · CPC title

• G06F16/24578

  using ranking · CPC title