Multiscale Process Model Research Articles

Investigating the structure-property correlations of pyrolyzed phenolic resin as a function of degree of carbonization.

Carbon-carbon (C/C) composites are attractive materials for high-speed flights and terrestrial atmospheric reentry applications due to their insulating thermal properties, thermal resistance, and high strength-to-weight ratio. It is important to understand the evolving structure-property correlations in these materials during pyrolysis, but the extreme laboratory conditions required to produce C/C composites make it difficult to quantify the properties in situ. This work presents an atomistic modeling methodology to pyrolyze a crosslinked phenolic resin network and track the evolving thermomechanical properties of the skeletal matrix during simulated pyrolysis. First, the crosslinked resin is pyrolyzed and the resulting char yield and mass density are verified to match experimental values, establishing the model's powerful predictive capabilities. Young's modulus, yield stress, Poisson's ratio, and thermal conductivity are calculated for the polymerized structure, intermediate pyrolyzed structures, and fully pyrolyzed structure to reveal structure-property correlations, and the evolution of properties are linked to observed structural features. It is determined that reduction in fractional free volume and densification of the resin during pyrolysis contribute significantly to the increase in thermomechanical properties of the skeletal phenolic matrix. A complex interplay of the formation of six-membered carbon rings at the expense of five and seven-membered carbon rings is revealed to affect thermal conductivity. Increased anisotropy was observed in the latter stages of pyrolysis due to the development of aligned aromatic structures. Experimentally validated predictive atomistic models are a key first step to multiscale process modeling of C/C composites to optimize next-generation materials.

Economic model predictive control for packed bed chemical looping combustion

This study presents an economic model predictive control (EMPC) scheme for the packed-bed reactor (PBR) design of a chemical looping combustion (CLC) system. This scheme is enabled by a pseudo-homogeneous process model, which accurately represents the macroscopic process behaviour, and enables economics to be optimized online. Both CLC stages are optimized whereby the oxidation stage considers energy generation and inert gas use, while the reduction stage economizes CO2 production and fuel use. To understand the behaviour of the EMPC, the scheme is tested on a CLC plant represented by a multi-scale process model. Both stages are tested under varying initial inlets, energy prices, carbon prices, and fuel prices. The optimal policy for the oxidation stage is found to be that of maximal peak temperature, where the EMPC results in revenue while a standard NMPC controller may result in losses. The optimal reduction stage behaviour is highly dependent on carbon and fuel prices whereby a trade-off between CO2 selectivity and throughput are observed. The EMPC approach is found to be economically superior to advanced regulatory control with up to an order-of-magnitude and ∼33% economic improvements in the oxidation and reduction stages, respectively. This study represents a step forward towards the adoption of the intensified PBR CLC process as an emerging and viable technology for heat generation with inherent CO2 capture.

Multiscale Process Modeling of Semicrystalline PEEK for Tailored Thermomechanical Properties.

Polyether ether ketone (PEEK) is a semicrystalline thermoplastic that is used in high-performance composites for a wide range of applications. Because the crystalline phase has a higher mass density than that of the amorphous phase, the evolution of the crystalline phase during high-temperature annealing processing steps results in the formation of residual stresses and laminate deformations, which can adversely affect the composite laminate performance. Multiscale process modeling, utilizing molecular dynamics, micromechanics, and phenomenological PEEK crystal kinetic laws, is used to predict the evolution of volumetric shrinkage, elastic properties, and thermal properties, as a function of crystalline phase evolution, and thus annealing time, in the 306-328 °C temperature range. The results indicate that lower annealing temperatures in this range result in a faster evolution of thermomechanical properties and shrinkage toward the pure crystalline values. Therefore, from the perspective of composite processing, it may be more advantageous to choose the higher annealing rates in this range to slow the volumetric shrinkage and allow PEEK stress relaxation mechanisms more time to relax internal residual stresses in PEEK composite laminates and structures.

Evolution of process-induced strain/stress field in plain woven composite antenna reflector by fusing multiscale numerical model and experimental data

This paper investigates the evolution of the process-induced strain/stress field in plain woven composite antenna reflectors by fusing the numerical simulation and the experimental data. In the numerical simulation, a new multiscale curing process model of plain woven composite reflectors is proposed for process-induced strain/stress prediction. Specifically, representative volume elements at three scales are established and solved to capture the effective properties of plain woven composites. Further, a novel interface layer with cure-dependent parameters is used to emulate the tool-part interaction. In the experiment, the fiber Bragg grating sensor network is embedded into the composite reflector to measure the process-induced strains. Some of the measurements are fused into the curing process model by the optimization-based fusion method. The fused model was used to predict the full-field strain and stress, and the results indicate that areas with larger curvatures experience greater strain and stress due to the interaction between the tool and the part.

Ab Initio Multiscale Process Modeling of Ethane, Propane and Butane Dehydrogenation Reactions: A Review

Olefins are among the most important structural building blocks for a plethora of chemical reaction products, including petrochemicals, biomaterials and pharmaceuticals. An ever-increasing economic demand has urged scientists, engineers and industry to develop novel technical methods for the dehydrogenation of parent alkane molecules. In particular, the catalysis over precious metal or metal oxide catalysts has been put forward as an alternative way route to thermal-, steam- and fluid catalytic cracking (FCC). Multiscale system modeling as a tool to theoretically understand processes has in the past decade period evolved from a rudimentary measurement-complementing approach to a useful engineering environment. Not only can it predict various experimentally obtained parameters, such as conversion, activity, and selectivity, but it can help us to simulate trends, when changing applicative operating conditions, such as surface gas temperature or pressure, or even support us in the search for the type of materials, their geometrical properties and phases for a better functional performance. An overview of the current set state of the art for saturated organic short chain hydrocarbons (ethane, propane and butane) is presented. Studies that combine at least two different dimensional scales, ranging from atomistic-, bridging across mechanistic mesoscale kinetics, towards reactor- or macroscale, are focused on. Insights considering reactivity are compared.

An inherent strain based multiscale modeling framework for simulating part-scale residual deformation for direct metal laser sintering

Residual distortion is a major technical challenge for laser powder bed fusion (LPBF) additive manufacturing (AM), since excessive distortion can cause build failure, cracks and loss in structural integrity. However, residual distortion can hardly be avoided due to the rapid heating and cooling inherent in this AM process. Thus, fast and accurate distortion prediction is an effective way to ensure manufacturability and build quality. This paper proposes a multiscale process modeling framework for efficiently and accurately simulating residual distortion and stress at the part-scale for the direct metal laser sintering (DMLS) process. In this framework, inherent strains are extracted from detailed process simulation of micro-scale model based on the recently proposed modified inherent strain model. The micro-scale detailed process simulation employs the actual parameters of the DMLS process such as laser power, velocity, and scanning path. Uniform but anisotropic strains are then applied to the part in a layer-by-layer fashion in a quasi-static equilibrium finite element analysis, in order to predict residual distortion/stress for the entire AM build. Using this approach, the total computational time can be significantly reduced from potentially days or weeks to a few hours for part-scale prediction. Effectiveness of this proposed framework is demonstrated by simulating a double cantilever beam and a canonical part with varying wall thicknesses and comparing with experimental measurements which show very good agreement.

In Silico Design of Materials and Processes: An Application of ICME to Carburizing Steels

The availability of high computational power and sophisticated mathematical modeling techniques has ushered in a new era of multiscale and multiphysics materials and process modeling to accelerate engineering decision making. These developments have led to the concept of integrated computational materials engineering (ICME) which aims for concurrent design and development of materials, processes and products by making use of detailed material modeling supported by supplementary data-driven modeling and multiobjective optimization techniques. The present work focuses on adopting an ICME approach for concurrent design of carburizing-grade steel and process using integrated process modeling and design exploration technique. An integrated, composition-dependent, microstructure-based modeling module is used to model the process route of carburizing–quenching–tempering for carburizing-grade steels. The module is used to explore various combinations of composition and process set-points for predicting different microstructure and property outputs. The output generated by this module is used to develop surrogate models which is then used in a solution space exploration framework to obtain the most suitable composition and process conditions that can result in the desired requirements associated with the properties of final product while maintaining different operational constraints. The results predicted by the proposed framework are discussed and the need for in silico design approach for materials and process development is highlighted.

Optimization of the Iron Ore Direct Reduction Process through Multiscale Process Modeling

Iron ore direct reduction is an attractive alternative steelmaking process in the context of greenhouse gas mitigation. To simulate the process and explore possible optimization, we developed a systemic, multiscale process model. The reduction of the iron ore pellets is described using a specific grain model, reflecting the transformations from hematite to iron. The shaft furnace is modeled as a set of interconnected one-dimensional zones into which the principal chemical reactions (3-step reduction, methane reforming, Boudouard and water gas shift) are accounted for with their kinetics. The previous models are finally integrated in a global, plant-scale, model using the Aspen Plus software. The reformer, scrubber, and heat exchanger are included. Results at the shaft furnace scale enlighten the role of the different zones according to the physico-chemical phenomena occurring. At the plant scale, we demonstrate the capabilities of the model to investigate new operating conditions leading to lower CO2 emissions.

Interaction of C and Mn in a ∑3 grain boundary of bcc iron

The interaction of alloying elements with migrating interfaces is a key aspect that determines microstructure evolution during thermo-mechanical processing of metals and alloys. Recent advances in atomic scale resolution characterization techniques and atomistic modelling have dramatically increased the potential to generate new knowledge on interfaces thereby enabling paradigm shifts in microstructure design approaches. Computational materials science now offers exciting opportunities to formulate multi-scale process models that bridge the gap between atomistic and continuums approaches. In particular, the development of advanced high strength steels with novel alloying concepts has motivated atomistic scale simulations to predict the interaction of selected alloying elements with grain boundaries and the austeniteferrite interface in iron. Most of these simulations have so far been carried out for binary systems with one solute species. The extension of this modelling work to multi-component systems is, however, essential in order to account for potential interactions between different alloying elements in industrial steels. Here, we present ab initio simulations for the interaction of C and Mn at a ∑3 boundary in bcc iron using density functional theory (DFT). The simulation results confirm the strong co-segregation of C and Mn that has been recently observed in atom probe tomography studies of the austenite-ferrite interface in Fe-Mn-C alloys.

Multiscale modeling and operation of PECVD of thin film solar cells

This work proposes a multiscale modeling and operation framework for plasma-enhanced chemical vapor deposition (PECVD) of thin film silicon solar cells with uniform thickness and film surface microstructure that optimizes light trapping. Specifically, we focus on a single-wafer parallel-electrode PECVD process with showerhead arrangement and develop a multiscale model capturing both the gas-phase reaction and transport phenomena that lead to the deposition of the thin film across the wafer as well as multiple microscopic models that describe the evolution of the thin film surface microstructure at equispaced, discrete spatial locations across the wafer. While the modeling of chemical reactions and transport-phenomena (both diffusion and convection) in the gas-phase adopts the continuum hypothesis and is based on two-dimensional in space partial differential equations, a novel microscopic model is developed for the a-Si:H thin film surface evolution, which accounts for four microscopic processes: physisorption, surface migration, hydrogen abstraction and chemisorption. A hybrid kinetic Monte Carlo (kMC) algorithm is utilized to reduce computational requirements without compromising the accuracy of established chemical models that account for interactions amongst physisorbed radicals, and the microscopic model fidelity is established though calibration with experimentally obtained growth rates and surface morphology data. The results of the multiscale process model indicate that in order to produce a thin film with a diameter of 20cm and a uniform thickness with surface microstructure that optimizes light trapping: (a) a sinusoidally grated wafer surface should be used in which the grating period and depth should correspond to values that lead to film surface roughness and height–height correlation length that are on the order of visible light wavelength range, and (b) the substrate temperature should be adjusted, along several concentric zones across the substrate, to compensate for a radially non-uniform deposition rate of the film on the wafer owing to gas-phase transport phenomena. Due to the dependence of film growth rate on substrate temperature, the wafer surface is separated into four concentric zones, each with an independent heating element. Extensive simulations demonstrate that the use of appropriate sinusoidal wafer grating and the regulation of substrate temperature provide a viable and effective way for the PECVD of thin film silicon solar cells with uniform thickness and film surface microstructure that optimizes light trapping.

On Operation of PECVD of Thin Film Solar Cells

This work proposes a multiscale model-based operation framework for large-area plasma-enhanced chemical vapor deposition (PECVD) of thin film silicon solar cells with uniform thickness and film surface microstructure that optimizes light trapping. The results of a multiscale process model indicate that in order to manufacture a thin film with a diameter of 20 cm and a uniform thickness with surface microstructure that optimizes light trapping: a) a sinusoidally-grated wafer surface should be used in which the grating period and depth should correspond to values that lead to film surface roughness on the order of visible light wavelength range, and b) the substrate temperature should be regulated, along several concentric zones across the substrate, to compensate for a radially non-uniform deposition rate of the film on the wafer owing to gas-phase transport phenomena. Due to the dependence of film growth rate on substrate temperature, the wafer surface is separated into four concentric zones, each with an independent heating element. Simulations demonstrate that the use of appropriate sinusoidal wafer grating and the regulation of substrate temperature provide a viable and effective way for the large-area PECVD of thin film silicon solar cells with uniform thickness and film surface microstructure that optimizes light trapping.

Industrial process system assessment: bridging process engineering and life cycle assessment through multiscale modeling

Conducting life cycle assessments (LCA) of complex multi-line, multi-product manufacturing facilities can be challenging. There is no standard method to assign or allocate resource use and environmental impacts to the individual products. The Industrial Process System Assessment method is presented to address this gap in current approaches for LCA allocation by making use of a multiscale model of process systems. Algorithms for the allocation procedure rectify process, ancillary, and non-process data from a facility such that inputs (e.g., raw materials) and outputs (e.g., emissions) can be assigned to a particular product from a particular line. The allocation approach demonstrates systematic multiscale process model development to avoid arbitrary allocation assumptions. This standardized method will enable LCA practitioners and process engineers to more clearly differentiate resource use and environmental impacts of co-products from complex facilities. The model could be further extended to provide product allocation based on individual unit operations when data are available.

An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling

The complexity of local and dynamic thermal transformations in additive manufacturing (AM) processes makes it difficult to track in situ thermomechanical changes at different length scales within a part using experimental process monitoring equipment. In addition, in situ process monitoring is limited to providing information only at the exposed surface of a layer being built. As a result, an understanding of the bulk microstructural transformations and the resulting behavior of a part requires rigorous postprocess microscopy and mechanical testing. In order to circumvent the limited feedback obtained from in situ experiments and to better understand material response, a novel 3D dislocation density based thermomechanical finite element framework has been developed. This framework solves for the in situ response much faster than currently used state-of-the-art modeling software since it has been specifically designed for AM platforms. This modeling infrastructure can predict the anisotropic performance of AM-produced components before they are built, can serve as a method to enable in situ closed-loop process control and as a method to predict residual stress and distortion in parts and thus enable support structure optimization. This manuscript provides an overview of these software modules which together form a robust and reliable AM software suite to address future needs for machine development, material development, and geometric optimization.

Effect of climate change on corrosion rates of structures in Australia

As structures built now will be expected to last well past 2064 (50 years) it is vital that the effect of climate change be considered in their design and material selection. In particular changes in the rate of corrosion of metal components must be considered. To this end this study estimates the maximum likely change in the corrosion rate for the year 2070 so it can be included in current design. Changes in corrosion are estimated for 11 coastal and inland locations in Australia. For each station the climatic data (3-hourly) in 2070 is estimated by modifying current data with probable changes based on two climate change models (CSIRO: CSIRO-Mk 3.5 and MRI: MRI-CGCM 3.2.2). The former is for high global warming rate and the later the A1FI scenario. This climatic data is then run the Corrosion "predictor" (a multi-scale process model) to predict corrosion at each location. It is found that significant changes occur with corrosion in coastal locations increasing substantially, in contrast the corrosion at inland locations will decrease moderately. The increase in coastal locations is associated with a greater build up of salt due to less frequent rain evens while the reduction in inland locations is associated with a reduction in RH and thus surface wetness.

Model-based optimization of sulfur recovery units

Multi-scale process modeling is very appealing methodology for process optimization since it highlights certain issues that remain unexplored with conventional methodologies and debottlenecks certain potentialities that remain unexploited in chemical plants. In this work, a kinetic model with 2400 reactions and 140 species is implemented in a proper reactor network to characterize the thermal furnace and the waste heat boiler of sulfur recovery units; the network with detailed kinetics is the kernel of a Claus process simulation that includes all the unit operations and the catalytic train. By doing so, reliable estimation of acid gas conversion, elemental sulfur recovery, and steam generation is achieved with the possibility to carry out an integrated process-energy optimization at the total plant scale.

Experimental studies on distributions of granule size, binder content and porosity in batch drum granulation: Inferences on process modelling requirements and process sensitivities

Batch granulation experiments on a lab-scale drum granulator for a Calcite/Polyvinyl alcohol in water (Calcite/PVOH–H 2O) system are presented in this study. Experimental studies were carried out to study the aggregation kinetics and mechanism for this granulation recipe, whilst investigating the effects of binder-to-solids ratio and drum load on the granule size, binder content and porosity distributions. In particular, the effect of formulation properties and the granulation operating conditions on the batch process dynamics and the end-granule properties are studied. The formulation properties considered include liquid surface tension, powder-liquid contact angle, dynamic yield stress, powder shape and liquid viscosity. The operating variables include the binder-to-solids ratio, binder addition duration and the binder addition mode. The sensitivity in the process and the non-homogeneity of key particle attributes (size, binder content, and porosity) is evident. The important process manipulations for feedback control and potential disturbances are identified, formulating a comprehensive control configuration for batch and continuous granulation, with the latter case being exemplified in Glaser et al. [T., Glaser, C.F.W., Sanders, F.Y., Wang, I.T., Cameron, R., Ramachandran, J.D., Litster, J.M.-H., Poon, C.D., Immanuel, F.J. Doyle, III, 2007. Model predictive control of drum granulation. Manuscript in preparation.]. The importance of multi-scale process models that link fundamental material properties with the granulation mechanisms and end-granule properties is also evident from the experiments. A three-dimensional population balance equation structure in terms of the particle size, binder content and porosity is confirmed to be an ideal framework for the process model.

Reduced order modeling and dynamic optimization of multiscale PDE/kMC process systems

The problem of dynamic optimization for multiscale systems comprising of coupled continuum and discrete descriptions is considered. The solution of such problems is challenging owing to large computational requirements of the multiscale process model. This problem is addressed by developing a reduced multiscale model. This is achieved by combining order reduction techniques for dissipative partial-differential equations with adaptive tabulation of microscopic simulation data. The multiscale process optimization problem is subsequently solved using standard search algorithms. The proposed method is applied to two representative catalytic oxidation processes where optimal inlet concentration profiles are computed to guide the microscopic system from one stable stationary state to another stable stationary state.

Multiscale theory for linear dynamic processes: Part 2. Multiscale model predictive control (MS-MPC)

A multiscale framework, defined in a time-scale domain, offers significant advantages in designing and deploying model predictive control (MPC). The resulting multiscale MPC (MS-MPC) allows variable prediction and control horizons, which are adjusted in real-time in conjuction with the actual external disturbances that affect process behavior and the characteristics of model uncertainty generated in the course of feedback control, and can overcome the theoretical and practical hurdles of fixed-horizon classical MPC. In addition, MS-MPC can incorporate, naturally, richer descriptions of external disturbances and model uncertainties over several distinct temporal scales (ranges of frequencies) and can accommodate, naturally and optimally, measurements at multiple sampling intervals and control actions at multiple rates. MS-MPC is based on the multiscale framework of analysis established in Part 1 of this series. It employs a wavelet-based transformation of states, inputs and outputs from the time-domain to a time-scale domain, and multiscale process models on homogeneous binary trees, which define the time-scale domain. Finally, MS-MPC is better suited to handle input and output constraints over varying lengths of time. A series of numerical examples will illustrate the MS-MPC approach and its advantages in designing and deploying feedback process control systems.

Multiscale optimization using hybrid PDE/kMC process systems with application to thin film growth

The problem of optimal time-constant and time-varying operation for transport-reaction processes is considered, when the cost functional and/or equality constraints necessitate the consideration of phenomena that occur over disparate length scales. Multiscale process models are initially developed, linking continuum conservation laws with microscopic scale simulators. Subsequently, order reduction techniques for dissipative partial-differential equations are combined with adaptive tabulation of microscopic simulation data to reduce the computational requirements of the optimization problem, which is then solved using standard search algorithms. The method is applied to a conceptual thin film deposition process to compute optimal substrate-surface temperature profiles that simultaneously maximize film-deposition-rate uniformity (macroscale objective) and minimize surface roughness (microscale objective) across the film surface for a steady-state process operation. Subsequently, optimal time-varying policies of substrate temperature and precursor inlet concentrations are computed under the assumption of quasi-steady-state process operation.

OPTIMAL OPERATION OF THIN FILM GROWTH WITH MULTISCALE PROCESS OBJECTIVES

The issue of optimal time-varying operation for transport-reaction processes is considered, when the cost functional and/or equality constraints necessitate the consideration of phenomena that occur over disparate length scales. Multiscale process models are initially developed, linking continuum conservation laws with microscopic scale simulators. Subsequently, order reduction techniques for dissipative partial-differential equations are combined with adaptive tabulation methods for microscopic simulators to reduce the computational requirements of the optimization problem, which is then solved using standard search algorithms. The method is demonstrated on a thin film deposition process, where optimal surface temperature profiles and inlet switching times that simultaneously maximize thickness uniformity and minimize surface roughness across the film surface are computed.