High-fidelity Process Research Articles

Influence of the disorder correlation length on localization and quantum state transference in tight-binding channels

We investigate in detail the transference of quantum states in a disordered channel. We consider a one-dimensional tight-binding model consisting of a source S connected to a receiver R throughout a disordered channel. The disorder distribution contains a single tunable spatial correlation length. We demonstrate that the disorder correlation length plays a relevant role within the localization properties of the channel. The hopping parameter between the sites S and R and the channel are also adjustable parameters. We investigate the possibility of transference of quantum states along this quantum channel model and describe the optimal conditions for the occurrence of a high fidelity process.

Mechanical-electric composite-driven transfer nanoimprint for anti-reflective optical diffuser

Optical diffusers are the core component of optical information systems; however, the difficulty in reducing reflection caused by its complex surface morphology severely restricts the performance of the application systems. This study proposed and demonstrated a mechanical-electric composite-driven transfer nanoimprint technique to develop an anti-reflective optical diffuser by imprinting subwavelength-tapered nanocones on the extremely uneven micro-concave surface of a diffuser. Successively loading optimized mechanical pressure and electric-field on the designed discretely-supported template, enough deformation facing the uneven substrate was generated, which was impossible in the single-factor driven nanoimprinting technology. Therefore, full contact was formed between the template and the irregular diffused surface containing tightly narrow and deep micro-concave pits. To guarantee the high fidelity of the fabricated nanostructures, pre-curing and back-etching processes were implemented during transfer imprinting, which were different from the traditional process. Based on the full-contact mechanism and high fidelity process, an anti-reflective optical diffuser was developed through transfer imprinting of the sub-wavelength tapered nanocones on the surface of the diffuser. Experimental tests showed that the developed anti-reflective optical diffuser exhibited high spectral transmittance, high light intensity uniformity, and good durability.

Centrosomal organization of Cep152 provides flexibility in Plk4 and procentriole positioning.

Centriole duplication is a high-fidelity process driven by Polo-like kinase 4 (Plk4) and a few conserved initiators. Dissecting how Plk4 and its receptors organize within centrosomes is critical to understand the centriole duplication process and biochemical and architectural differences between centrosomes of different species. Here, at nanoscale resolution, we dissect centrosomal localization of Plk4 in G1 and S phase in its catalytically active and inhibited state during centriole duplication and amplification. We build a precise distribution map of Plk4 and its receptor Cep152, as well as Cep44, Cep192, and Cep152-anchoring factors Cep57 and Cep63. We find that Cep57, Cep63, Cep44, and Cep192 localize in ninefold symmetry. However, during centriole maturation, Cep152, which we suggest is the major Plk4 receptor, develops a more complex pattern. We propose that the molecular arrangement of Cep152 creates flexibility for Plk4 and procentriole placement during centriole initiation. As a result, procentrioles form at variable positions in relation to the mother centriole microtubule triplets.

Modeling and Control of Nonlinear Processes Using Sparse Identification: Using Dropout to Handle Noisy Data

Sparse identification of nonlinear dynamics (SINDy) is a recent nonlinear modeling technique that has demonstrated superior performance in modeling complex time-series data in the form of first-order ordinary differential equations (ODEs), which are explicit and continuous in time. However, a crucial step in the SINDy algorithm involves estimating the time derivative of the states from the discrete, measured data. Therefore, the presence of noise can greatly deteriorate the performance if it is not carefully considered and accounted for. In this work, SINDy is used with ensemble learning, where multiple models are identified to improve the overall/final nonlinear model’s performance. Specifically, in the SINDy algorithm, a fraction of the library functions considered for the ODE model representation are randomly dropped out in each submodel to favor model sparsity and stability at the possible risk of lowering the model accuracy. This trade-off is controlled by manipulating the fraction of the library functions dropped out and the total number of models generated, both of which are considered as hyperparameters to be tuned in the proposed algorithm. Data from open-loop simulations of a large-scale chemical plant are generated using the well-known high-fidelity process simulator, Aspen Plus Dynamics, and corrupted with substantial sensor noise to be implemented in the newly proposed algorithm, dropout-SINDy. The dropout-SINDy models obtained from training with the noisy data are then tested in open-loop simulations to demonstrate accurate identification of the steady-state and reasonably close transient behavior under a variety of initial conditions and manipulated input values. Finally, the constructed models are used in a Lyapunov-based model predictive controller to control the large-scale Aspen process, meeting desired closed-loop stability and performance specifications.

Physics-informed machine learning modeling for predictive control using noisy data

Due to the occurrence of over-fitting at the learning phase, the modeling of chemical processes via artificial neural networks (ANN) by using corrupted data (i.e., noisy data) is an ongoing challenge. Therefore, this work investigates the effect of both Gaussian and non-Gaussian noise on the performance of process-structure based recurrent neural networks (RNN) models, which take the form of partially-connected RNN models in this work, that are used to approximate a class of multi-input-multi-outputs nonlinear systems. Furthermore, two different techniques, specifically Monte Carlo dropout and co-teaching, are utilized in the development of partially-connected RNN models. These two techniques are employed to reduce the over-fitting in ANNs when noisy data is used in the training process and, hence, to improve the open-loop accuracy as well as the closed-loop performance under a Lyapunov-based model predictive controller (MPC). Aspen Plus Dynamics, a well-known high-fidelity process simulator, is used to simulate a large-scale chemical process application in order to demonstrate the anticipated improvements in both open-loop approximation and closed-loop controller performance in the presence of Gaussian and non-Gaussian noise in the data set using physics-informed RNNs.

A part-scale, feature-based surrogate model for residual stresses in the laser powder bed fusion process

Over the past decade, the Laser Powder Bed Fusion (LPBF) process has been widely used in the fabrication of industrial parts with advanced functions. It is known that the complex thermal processing of the material during the LPBF process has a significant influence on product quality. While high fidelity simulation models can account for the effects of processing, they are generally too computationally expensive to be directly used in the design of components. Consequently, in this paper we propose a surrogate model for Simulation Models of the residual stress at the part-scale based on a Convolutional Neural Network (CNN) with a 3D U-Net architecture. In order to model the wide range of geometries that can arise during the design process, we developed a feature-based approach in which we trained our CNN on combinations of three basic types of geometric features: circular struts, square struts, and walls. Data augmentation was utilized to account for orientation invariance. Several benchmarks were designed to test the performance of the surrogate model. Results demonstrated that a CNN with a 3D U-Net architecture can accurately predict the residual stress for the features designed. The average training and testing errors are 5.3% and 6.6%, respectively. Prediction performance for the benchmark parts led to validation errors of 14.4%− 28.3% due to their complex geometries. Nevertheless, this strategy led to a significant reduction in runtime, demonstrating that the proposed feature-based surrogate model has the potential to replace high fidelity process simulations for the design of practical engineering parts manufactured using LPBF.

Impact of turbulent inflow and orography on the low-frequency noise sources of a wind turbine

A high-fidelity process chain is used to numerically investigate the low-frequency emissions from a generic wind turbine under turbulent inflow conditions. It consists of the Computational Fluid Dynamics (CFD) solver FLOWer, which is coupled to the multi-body simulation software SIMPACK to take unsteady aeroelastic effects into account. A realistic flow field for the complex terrain of Perdigão, Portugal is obtained by including the orography and vegetation in the simulation and using a precursor simulation with E-Wind to generate a site- and situation-specific inflow. The acoustic emissions are calculated with the Fflowcs-Williams-Hawkings (FW-H) acoustic solver ACCO. The simulations show that the tower emits noise caused by the blade-tower interaction (BTI) equally strong in all directions. To the sides of the turbine, this contribution is dominant, regardless of the turbulent inflow. The blades, on the other hand, emit significantly more noise under turbulent inflow, especially in streamwise direction, where they become the dominant source. The main noise mechanism here is the low-frequency part of the inflow turbulence (IT), followed by the BTI. The flow field in Perdigão causes additional large-scale variations in IT noise over time.

Refined Simulation Method for Computer-Aided Process Planning Based on Digital Twin Technology.

Simulation technology is widely used in computer-aided process planning (CAPP). The part machining process is simulated in the virtual world, which can predict manufacturing errors and optimize the process plan. Simulation accuracy is the guarantee of process decision-making and optimization. This article focuses on the use of digital twin technology to build a high-fidelity process model, taking the advantage of the integration of multiple systems, in order to achieve the dynamic association of real-time manufacturing data and process models. Making use of the CAPP/MES systems, the surface inspection data of the part is fed back to the CAPP system and associated with the digital twin process model. The wavelet transform method is used to reduce the noise of the high-frequency signal of the detection data, and the signal-to-noise ratio (SNR) is calculated to verify the noise reduction effect. The surface topography, after noise reduction, was reconstructed in Matlab. On this basis, the Poisson reconstruction algorithm is used to reconstruct the high-fidelity process model for the refined simulation of the subsequent processes. Finally, by comparing the two sets of simulation experiments with the real machining results, we found that the simulation results, based on the digital twin model, are more accurate than the traditional simulation method by 58%.

PRESTO: Predictive REcommendation of Surrogate models To approximate and Optimize

Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate. Many techniques exist for surrogate modeling; however, selecting suitable techniques for a given application remains an open challenge. This work describes PRESTO, a Random Forest classifier-based tool, to recommend appropriate surrogate modeling techniques for a given dataset for surface approximation and surrogate-based optimization, using attributes calculated only using the input and output data. The tool identifies the techniques for surface approximation with an accuracy of 91% and a precision of 90% and for surrogate-based optimization with an accuracy of 98% and a precision of 99%. PRESTO was tested on data generated from a high fidelity process model of the cumene production process. Its performance on this case study was comparable to the training data. PRESTO enables computational time savings for selecting surrogate model forms by avoiding expensive trial-and-error methods.

An Integrated Computational Materials Engineering Predictive Platform for Fatigue Prediction and Qualification of Metallic Parts Built With Additive Manufacturing

Abstract Additive manufacturing (AM) processes create material directly into a functional shape. Often the material properties vary with part geometry, orientation, and build layout. Today, trial-and-error methods are used to generate material property data under controlled conditions that may not map to the entire range of geometries over a part. Described here is the development of a modeling tool enabling prediction of the performance of parts built with AM, with rigorous consideration of the microstructural properties governing the nucleation and propagation of fatigue cracks. This tool, called DigitalClone® for additive manufacturing (DCAM), is an Integrated Computational Materials Engineering (ICME) tool that includes models of crack initiation and damage progression with the high-fidelity process and microstructure modeling approaches. The predictive model has three main modules: process modeling, microstructure modeling, and fatigue modeling. In this paper, a detailed description and theoretical basis of each module is provided. Experimental validations (microstructure, porosity, and fatigue) of the tool using multiple material characterization and experimental coupon testing for five different AM materials are discussed. The physics-based computational modeling encompassed within DCAM provides an efficient capability to fully explore the design space across geometries and materials, leading to components that represent the optimal combination of performance, reliability, and durability.

Waste respirator processing system for public health protection and climate change mitigation under COVID-19 pandemic: Novel process design and energy, environmental, and techno-economic perspectives

The ongoing COVID-19 pandemic leads to a surge on consumption of respirators. This study proposes a novel and effective waste respirator processing system for protecting public health and mitigating climate change. Respirator sterilization and pre-processing technologies are included in the system to resist viral infection and facilitate unit processes for respirator pyrolysis, product separation, and downstream processing for greenhouse gas (GHG) emission reduction. We evaluate the system’s environmental performance through high-fidelity process simulations and detailed life cycle assessment. Techno-economic analysis results show that the payback time of the waste respirator processing system is seven years with an internal rate of return of 21.5%. The tipping fee and discount rate are the most influential economic factors. Moreover, the unit life cycle GHG emissions from the waste respirator processing system are 12.93 kg CO2-eq per thousand waste respirators treated, which reduces GHG emissions by 59.08% compared to incineration-based system so as to mitigate climate change.

A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong

Overcrowding at emergency departments in Hong Kong has been a critical issue for hospital managers recently. In this study, we focus on optimizing the medical staff configuration to alleviate overcrowding. According to the service requirements proposed by the Hong Kong government, 90% of urgent patients should receive treatment within 30 min. However, this condition is rarely satisfied in the practical situation. Therefore, we formulate the problem as minimizing the proportion of urgent patients that violate the service requirements while satisfying the service requirements of the other categories and cost constraints, thereby resulting in an optimization problem with a stochastic objective and several stochastic constraints. To solve this problem efficiently, we proposed a multi-fidelity simulation optimization framework containing a low- and a high-fidelity process. We utilize an evolutionary algorithm with violation-constrained handling assisted by a surrogate model as a low-fidelity process to shrink the solution space and generate an elite population. In the high-fidelity process, we exploit the optimal computing budget allocation method to identify the best solution in the elite population based on a data-driven simulation model. A case study is also discussed, and the results demonstrated that with a limited labor cost, there is a 52.05% reduction on average in the waiting time of urgent patients. Meanwhile, our proposed multi-fidelity simulation optimization framework proves to save 98.4% of the simulation time.

Stochastic simulation-based superstructure optimization framework for process synthesis and design under uncertainty

Advances in simulation and optimization technologies coupled with the continued growth in computing power now increasingly pave the way for the development of advanced model-based engineering design frameworks. In this work, we propose an extensive computational framework, which brings together state-of-the-art engineering practices, such as high fidelity process simulation, superstructure-based conceptual design, global sensitivity analysis, Monte Carlo procedures for uncertainty quantification, and a stochastic simulation-based design space optimizer in order to foster decision making under uncertainty. The capabilities of the framework are highlighted in a case study, which addresses the challenges of how to synthesize and design wastewater treatment plant configurations under influent uncertainties. In order to handle multiple stochastic constraints, a black-box solver using a new infill criterion for surrogate-based optimization is also proposed. The results demonstrate the promising potential of the simulation and sampling-based framework for effectively addressing stochastic design problems arising in broader engineering domains.

CLL-384: Proliferation and Unmutated IGHV Status are Associated with Homologous Recombination Activity in CLL Cells

In IgHV-unmutated CLL (U-CLL), the proliferation rate is higher compared with IGHV-mutated CLL (M-CLL). Yet, why CLL cells differ in their mutational rates is unknown. Accumulating mutations in the IGHV gene lead to subsequent accumulation of DNA breaks. In rapidly dividing cells, DNA breaks are repaired by the efficient high-fidelity homology-directed DNA (HR) repair apparatus, whereas in slowly dividing cells, they are repaired by the inefficient low-fidelity nonhomologous end-joining (NHEJ) repair mechanism. Since HR is a high-fidelity DNA repair process, we expect that in cells that preferentially utilize HR, the mutations will be substituted by non-mutated (“germline”) nucleotides. We therefore postulate that proliferating U-CLLs utilize HR, which corrects the mutations accumulated during SHM. To test whether U-CLL cells preferentially use HR, we incubated CLL cells from two patients with U-CLL and two patients with M-CLL with antibodies that bind RPA and RAD51, key components in the HR repair pathway. By fluorescence microscopy, we detected these proteins in U-CLL, but not M-CLL, cells. To confirm these findings, we quantified the transcript levels of key players in the DNA repair process by qRT-PCR in four patients with M-CLL and two patients with U-CLL. Consistent with the immunohistochemistry data, the levels of FANCD2, RAD51, BRCA1 and BRCA2, which represent HR, were significantly higher in U-CLL compared with M-CLL. Levels of XRCC5, XRCC6, and PRKDC, which represent the NHEJ machinery, were low across all samples tested. Since HR is only operative in dividing cells, we hypothesized that inducing proliferation will turn on the HR enzymatic machinery. To induce proliferation, we incubated CLL cells from eight patients with CpG oligonucleotide. Using this agonist, proliferation increased by 110% (range: 42 to 250) in M-CLL cells but only by 25% (range 14 to 66) in U-CLL cells. The increase in proliferation rates correlated with upregulation of the HR machinery at the RNA and protein levels as measured by qRT-PCR and immunohistochemistry, respectively. Summary Here, we show that proliferating CLL cells utilize more often HR recombination repair machinery. Whether HR activity contributes to reduced mutational rates in the IGHV gene remains to be determined.

Techno-economic Feasibility of Thermochemical Conversion Pathways for Regional Agricultural Waste Biomass

This study examines four prominent thermochemical conversion technologies such as slow pyrolysis (SP), fast pyrolysis (FP), gasification (GA) and hydrothermal liquefaction (HTL), for treating poultry litter in New York State (NYS). Nine cases involving various combinations of the four technologies and different downstream processing options such as bio-oil upgrading, Fischer-Tropsch conversion and combined heat and power generation are chosen based on the product composition and distribution. High-fidelity process simulations for each technological platform are performed to derive the mass and energy balance information. Calculations and breakdown of the equipment costs, capital costs, operating and maintenance costs and utilities are provided and compared extensively for each of the nine cases. The economic performance is further analyzed by calculating and comparing the resultant net present values (NPV), ranging from $10 M to $170 M (SP), $89 M to $314.5 M (FP), $28 M to $196 M (HTL) and $25 M to $234 M (GA). The greenhouse gas emission inventories are also compiled to understand the corresponding impacts of different downstream processing choices (ranging from 217 to 494.5 kg CO2-eq/t feedstock with both the pyrolysis technologies outperforming the others in most cases) and to highlight the trade-off with economic performance. Through sensitivity analysis, the influential factors requiring further investigation are identified and it is found that plant capacity and bio-oil yield are the most important parameters for the fast pyrolysis systems, biochar price for SP is the single most important parameter.

Base Excision Repair in the Immune System: Small DNA Lesions With Big Consequences.

The integrity of the genome is under constant threat of environmental and endogenous agents that cause DNA damage. Endogenous damage is particularly pervasive, occurring at an estimated rate of 10,000–30,000 per cell/per day, and mostly involves chemical DNA base lesions caused by oxidation, depurination, alkylation, and deamination. The base excision repair (BER) pathway is primary responsible for removing and repairing these small base lesions that would otherwise lead to mutations or DNA breaks during replication. Next to preventing DNA mutations and damage, the BER pathway is also involved in mutagenic processes in B cells during immunoglobulin (Ig) class switch recombination (CSR) and somatic hypermutation (SHM), which are instigated by uracil (U) lesions derived from activation-induced cytidine deaminase (AID) activity. BER is required for the processing of AID-induced lesions into DNA double strand breaks (DSB) that are required for CSR, and is of pivotal importance for determining the mutagenic outcome of uracil lesions during SHM. Although uracils are generally efficiently repaired by error-free BER, this process is surprisingly error-prone at the Ig loci in proliferating B cells. Breakdown of this high-fidelity process outside of the Ig loci has been linked to mutations observed in B-cell tumors and DNA breaks and chromosomal translocations in activated B cells. Next to its role in preventing cancer, BER has also been implicated in immune tolerance. Several defects in BER components have been associated with autoimmune diseases, and animal models have shown that BER defects can cause autoimmunity in a B-cell intrinsic and extrinsic fashion. In this review we discuss the contribution of BER to genomic integrity in the context of immune receptor diversification, cancer and autoimmune diseases.

Thermal Analysis for Simulation of Metal Additive Manufacturing Process Considering Temperature- and History-Dependent Material Properties

Additive manufacturing (AM) technology is increasingly being used in the aerospace industry due to its advantages for aerospace components such as reduction of weight. A deep understanding of the behavior and properties of additively manufactured materials or parts is required to effectively carry out the certification process which is inevitable for aerospace components. However, since AM has so many parameters that affect the performance of products, the help of high-fidelity process simulation techniques is essential to fully analyze and understand their effects. In this research, we propose a new method to effectively implement the thermal analysis for process simulations of laser powder-bed fusion technique, a representative AM technique for metal materials, using existing commercial finite element analysis software. Thermal analysis for simulations of AM process is performed and the melt pool size is compared with test results to verify the accuracy of the simulation. In AM process simulations, material properties may vary significantly with temperature, and they are also dependent on the temperature history of the material because whether the current state is a powder or solid state is determined by the maximum temperature value in the past temperature history. Therefore, in this paper, user-defined subroutines and field variables are implemented so that the temperature history of each integration point for the finite element analysis can be properly tracked and appropriate material properties can be assigned accordingly. Using the proposed methods, thermal analysis for AM process simulations can be performed successfully with good accuracy compared with the existing test results.

Techno-Economic Feasibility and Spatial Analysis of Thermochemical Conversion Pathways for Regional Poultry Waste Valorization

This study examines prominent thermochemical conversion technologies, such as slow pyrolysis, fast pyrolysis, gasification, and hydrothermal liquefaction, for treating poultry litter in New York State (NYS). Nine cases involving combinations of the four technologies and different downstream processing options such as bio-oil upgrading and Fischer–Tropsch conversion are chosen based on the product distribution. High-fidelity process simulations are performed to derive the mass and energy balance. Economic performance for the nine cases varied widely with largely overlapping net present values, ranging from $10MM to $170MM (slow pyrolysis), $89MM to $314.5MM (fast pyrolysis), $28MM to $196MM (hydrothermal liquefaction), and $25MM to $234MM (gasification). Both pyrolysis technologies had 18% to 56% lower greenhouse gas (GHG) emissions than the other technologies. GHG balances showed trade-offs with economic performance. Sensitivity analysis identified carbon credits, products’ market price, and plant capacity as the most influential factors. Building one centralized biorefinery in NYS especially for fast pyrolysis was more economically feasible than building multiple smaller biorefineries (biochar breakeven price of −$128 to −$91/ton vs $74 to $93/ton). The trend for slow pyrolysis was similar but with comparatively little difference (biochar breakeven price of $59 to $96/ton for one biorefinery vs $76 to $91/ton for multiple biorefineries).

The poly-thymine based DNA photolithography onto electrostatic coupling substrates.

In order to develop a rapid and high fidelity process for DNA self-assembly with patterning, the pattern of thymine dimerization is presented onto electrostatically bound DNA substrate by photolithography. The ability of binding for the process, which is attenuated conditions such as contact with photomask and washing by solution buffer is evaluated by X-ray photoelectron spectroscopy (XPS). Through thymine dimerization and hybridization chain reaction (HCR), DNA patterns, including multi-patterns, are demonstrated. For expansion to protein molecular patterning, the target DNA is tethered to biotin, allowing patterning with streptavidin linked fluorophores such as Cy3-streptavidin and phyecocayine-streptavidin.

Ultra-deep sequencing reveals pre-mRNA splicing as a sequence driven high-fidelity process.

Alternative splicing diversifies mRNA transcripts in human cells. While the spliceosome pairs exons with a high degree of accuracy, the rates of rare aberrant and non-canonical pre-mRNA splicing have not been evaluated at the nucleotide level to determine the quantity and identity of these events across splice junctions. Using ultra-deep sequencing the frequency of aberrant and non-canonical splicing events for three splice junctions flanking exon 7 of SMN1 were determined at single nucleotide resolution. After correction for background noise introduced by PCR amplification and sequencing steps, pre-mRNA splicing was shown to maintain a low overall rate of aberrant and non-canonically spliced events. Several previously unannotated splicing events across 3 exon|intron junctions in SMN1 were identified. Mutations within SMN exon 7 were shown to affect splicing fidelity by modulating RNA secondary structures, by altering the binding site of regulatory proteins and by changing the 5’ splice site strength. Mutations also create a truncated SMN1 exon 7 through the introduction of a de novo non-canonical 5’ splice site. The results from the ultra-deep sequencing approach highlight the impressive fidelity of pre-mRNA splicing and demonstrate that the immediate sequence context around splice sites is the main driving force behind non-canonical splice site pairing.