System Integration
Liang Yu
Department of Biological Systems Engineering
Washington State University
03. 21. 2013
Outline of this class
Multi-scale system integration
Introduction of concept
Application
Materials Engineering
Biological System
Chemical Engineering
Process integration
Introduction of history and definition
Important method — pinch analysis
Summary
Multi-scale system integration
Introduction
Multiscale modeling
the field of solving physical problems which
have important features at multiple scales,
particularly multiple spatial and(or) temporal
scales in engineering, mathematics, physics,
meteorology and computer science
Multiscale Nature of Matter
Physical Scales (Spatial scale)
Temporal Scales
Physical Scales
Discrete Nature of
Matter
Multiple Physical
(Spatial) scales
Exist
Example: River
Physical Scale:
km = 103 m
Physical Scales
• Water Drops
• Physical Scale:
– mm = 10-3 m
• Water Cluster
• Physical Scale:
– 5 nm = 5 x 10-9 m
Physical Scales
Water Molecule
Physical Scale:
0.278 nm = 2.78 x 10-10 m
Methods for Multiscale Modelling
Sequential Methods
Separation of length and time scales
Parameter passing, kinetic Monte-Carlo(KMC)
Concurrent Methods
Different length and time scales within hybrid
scheme
Typically Molecular Dynamics (MD), continuum (FE);
Level set
Coarse Graining
Integration over fast time scales short length scales
Temporal Scales
Multiple Time Scales in
Matter
Time Scale of Interest
Depends on Phenomenon of
Interest
Fluid Time Scales:
River Flow: hours
Rain Drop Falling: 30-60 min
Water Molecule Interactions:
fractions of a second
Different Scales, Different Laws
Governing Equations different for different
scales
Example: Modeling a Fluid:
River Flow: Navier-Stokes Equations
Interactions between fluid particles: Newton’s
Molecular Dynamics
Atomic, Subatomic Description of Fluid
Molecule: Schrödinger’s equations
Research in Multi-scale Modeling
Materials Engineering
Biological System
Chemical Engineering
Materials Engineering
(Nano Mechanics and Materials)
•
Structural and material design
•
Optimization
•
Prediction and validation
Nano- and micro-structure
Electronic structure
Molecular mechanics
Continuum
mechanics
Potentials
Const.
laws
Plasticity
Multiscale methods
Computations
and design
Manufacturing
platform
Function
Performance
Reliability
Prediction
Validation
Examples of Multi-Scale Phenomena in Solids
Shear bands
Mechanics of carbon nanotubes
17:1
250:1
250:1
200 m
Figures: D. Qian, E. Karpov, NU
Shaofan Li, UC-Berkeley
Movie: Michael Griebel, Universität Bonn
Limitations of industrial simulations
Continuum models are good, but not always adequate
Problems in fracture and failure of solids require improved constitutive models to
describe material behavior
Macroscopic material properties of new materials and composites are not readily
available, while they are needed in simulation-based design
Detailed atomistic information is required in regions of high deformation or
discontinuity
Molecular dynamics simulations
Limited to small domains (~106-108 atoms) and small time frames
(~nanoseconds)
Experiments, even on nano-systems, involve much larger systems over longer
times
Opportunities:
Obtain material properties by subscale (multi-scale) simulation
Enrich information about material/structural performance across scales via
concurrent multi-scale methodologies
Approaches for multi-scale
Hierarchical approach
Use known information at one scale to generate model for larger
scale
Information passing typically through some sort of averaging
process
Example: bonding models/potentials, constitutive laws
Concurrent approach
Perform simulations at different length scales simultaneously
Relationships between length scales are dynamic
Classic example: “heat bath” techniques
Macroscopic, Atomistic, Ab Initio Dynamics (MAAD)
Finite elements (FE),
molecular dynamics (MD), and
tight binding (TB) all used in a
single calculation (MAAD)
MAAD = macroscopic,
atomistic, ab initio dynamics
Atomistics used to resolve
features of interest (crack)
Continuum used to extend size
of domain
Developed by Abraham,
Broughton, and co-workers
From Nakano et al, Comput. In Sci. and Eng., 3(4) (2001).
MAAD: Concurrent Coupling of Length Scales
• Scales are coupled in
“handshake” regions
• Finite element mesh
graded down to atomic
lattice in the overlap
region
• Total Hamiltonian is
energy in each domain,
plus overlap regions
H Tot  H FE  u, u   H FE / MD  u, u, r, r 
Broughton, et al, PRB 60(4) (1999).
Handshake
at MD/FE
interface
 H MD  r, r   H MD / TB  r, r 
 H TB  r, r 
Nakano et al,
Comput. In Sci. and
Eng., 3(4) (2001).
Challenge
Large number of degrees of freedom at the atomic scale
Interfaces: mismatch of dynamic properties, and other issues
Consistent and accurate representation of meso-, micro- & nanolevel behavior within continuum models
Interdisciplinary nature of multiscale methods
continuum mechanics
classical particle dynamics (MD), and lattice mechanics
quantum mechanics and quantum chemistry
thermodynamics and statistical physics
Atomic scale plasticity: lattice dislocations
Dynamics of infrequent events: diffusion, protein dynamics
Algorithmic issues in large scale coupled simulations
Biological System
The study of life is extremely complex
Biologists have become highly
specialized in their fields
Fields are divided by scales:
Molecular Biology
Cell Biology
Organism Biology
Population Biology
Multiscale Modeling
Math. Biology also sliced into distinct scales
Models created for particular scale of interest
Finer scale processes often govern processes
seen at coarser scales and vice versa
One scale models quite sophisticated
Multiscale Modeling: combining models from
different scales, still in its early stages of
development
Whole Brain Modeling Levels of Detail
Structural
Individual neuron morphologies, channels,
synapses,
Spines
Tissue, layers, columns, areas, nuclei, systems,
Local circuits, long-range small-world connectivity
Functional
Single ion channel
Firing properties
Synaptic responses
Plasticity, neuromodulation
Connectivity, micro- and macro-circuits
Network dynamics
Behavior, perception, psychophysics
Stimulation
Physiological, e.g. sensory organs
• Electrical, magnetic stimulation
Pharmacological
Imaging data for mouse
brain at EM resolution: 30
PetaVoxels
Multi-scale and measurement
Example: Cellular Level Brain Model
Human Brain
Multiscale Modeling Strategies
Assuming Quasi-Equilibrium:
Microscopic Scale, fast process
Macroscopic Scale, slow process
Assume quasi-equilibrium of micro
Use info in constitutive laws at macro scale
Example: Complex Fluid (Ren, 2005)
Constitutive Law: Momentum Flux Equation
Microscopic Level: Molecular Dynamics used on fluid
particles to estimate viscous stress for macro
Multiscale Modeling Strategies
Time Splitting:
Microscopic Scale, slow process
Macroscopic Scale, fast process
Split the simulation into two time scales
Example: Thrombus Development (Xu, 2008)
Fast, macroscopic blood flow solved first
Used as boundary conditions for slow thrombus
growth, modeled by Cellular Potts model
(accumulation of cells via probabilities)
Multiscale Modeling Strategies
In all strategies the common goal is to create
constitutive laws and continuum-level equations
of a biological system, whose parameters are
computed from finer scale models of the system
Microscopic
Continuum
Chemical Engineering
Chemical engineering is a diverse and evolving
disciplinary dealing with dynamic structures, typically
nonlinear, non-equilibrium and hierarchically multi-scale
in nature.
Scale-up is not only a major challenge for chemical
engineers and scientists but also crucial to the survive of
the chemical companies.
There are the vast span of spatial scales and time scales
Few in depth explorations have been made in the field of
chemical engineering
Hierarchical multi-levels and multi-scales of spatiotemporal structures in chemical engineering
Li, Jinghai, et al. "Multi-scale compromise and multi-level correlation in complex systems." Chemical Engineering Research and Design 83.6
(2005): 574-582.
Intersection of chemistry and
chemical engineering
Multi-scale methods (1)
Descriptive method: the most popular approach simply distinguishes the various structures at
different scales without, however, revealing the physical relationship between different scales and
the underlying mechanism of multi-scale structure. It is used mainly for stationary structures, or for
dynamic structures that change very slowly.
Multi-scale methods (2)
Correlative method: the basic idea is to find out the explicit correlations between neighbouring
scales and then provide a complete multi-scale description of the system by cascading these
correlations. Its idea has been well exemplified by the measurement of fluid properties through
molecular dynamics simulations, which is then fed to numerical calculations of the Navier–Stokes
equation to predict global properties.
Multi-scale methods (3)
Analytical method: reveal the dominant mechanisms of the structure and the relationship between
the scale. The example is the Energy Minimization Multi-Scale (EMMS) model.
Multi-phase Reaction Laboratory, Institute of Process Engineering, Chinese Academy of Sciences, Beijing P.R. China
Multi-scale simulation of gas-solid
two-phase flow
Process integration
History of Process Integration
Bodo Linnhoff (born 1948,chemical engineer
and entrepreneur) started the area of pinch
(bottleneck identification) at The University of
Manchester Institute of Science and Technology
(UMIST) in the 60’s, focusing on the area of
Heat Integration
UMIST Dept of Process Integration was created
in 1984, shortly after the consulting firm
Linnhoff-March Inc. was formed
Definition of process integration
Process integration (process synthesis) is primarily regarded
as process design (both new and retrofits design), but also
involve planning and operation. The methods and systems are
applied to continuous, semi-batch, and batch process.
In addition to thermodynamics (the foundation of pinch), other
techniques are being drawn upon for holistic analysis, in
particular:
Process modeling
Process statistics
Process optimization
Process economics
Process control
Process design
Business objectives currently driving the
development of PI
Emphasis is on retrofit projects in the “new economy” driven
by Return on Capital Employed (ROCE)
PI is “Finding value in data quality”
Corporations wish to make more knowledgeable decisions:
For operations,
During the design process.
Lower capital cost design, for the same design objective
Incremental production increase, from the same asset base
Marginally-reduced unit production costs
Better energy/environmental performance, without
compromising competitive position
Overview of Process Integration Tools
Business Model And
Supply Chain Modeling.
Pinch Analysis
Optimization by
Mathematical
Programming
Stochastic Search
Methods
Process Simulation
Life Cycle Analysis
•Dynamic
Data-Driven Process
Modeling
Integrate Process
Design and Control
•Steady state
Data Reconciliation
Process Data
Real Time Optimization
What can be integrated?
(The onion diagram for process synthesis)
Example for Process Integration
Pinch Analysis
Pinch analysis is a methodology for minimizing energy consumption
of chemical processes by calculating thermodynamically feasible
energy targets (or minimum energy consumption) and achieving
them by optimising heat recovery systems, energy supply methods
and process operating conditions. It is also known as process
integration, heat integration, energy integration or pinch technology.
The Pinch analysis is a technique to design:
Recovery Networks (Heat and Mass)
Utility Networks (so called Total site Analysis)
The basis of Pinch Analysis:
The use of thermodynamic principles (first and second law).
The use heuristics (insight), about design and economy.
Example for Pinch Analysis
Method of Pinch Analysis
Phases of Pinch analysis
The Four phases of pinch analysis in the design of recovery process:
Which involves collecting
data for the process and the
utility system
Process
Simulation
Where an initial Heat
Exchanger Network is
established by heuristics tools
allowing a minimum target to
be reached.
Data Extraction
Targeting
Which establishes figures for
the best performance in
various aspects.
Design
Where an initial design is
simplified and improved
economically.
Optimization
Applications of Pinch Analysis
Heat Exchange Networks (Bodo Linnhoff,
1960’s)
Mass Exchange Networks (El-Halwagi and
Manousiouthakis, 1987)
Water pinch (Yaping Wang and Robin
Smith, 1994; Nick Hallale, 2002; Prakash
and Shenoy, 2005)
Hydrogen pinch (Nick Hallale et al., 2003;
Agrawal and Shenoy, 2006)
Summary
Summary
Multiscale optimal synthesis for the biorenewable conversion process
Z. Yuan, B. Chen. Process synthesis for addressing the sustainable energy systems and environmental issues. AIChE
Journal Volume 58, Issue 11, pages 3370–3389, November 2012
Summary
Process integration for catalytic biorenewable conversion