Multi-level Optimization Strategy Research Articles

Aerodynamic optimization design and experimental verification of a high-load axial flow compressor

The performance and stable operating range of compressors are critical to the efficient operation of various turbomachinery systems. This paper proposes a multi-level optimization strategy combining the uni-uniform direct free deformation method, Linux partitioned CPU accelerated parallel computing technology, multi-objective particle swarm optimization algorithm and downhill simplex algorithm, which improves design efficiency. Numerical results show that after optimization design, the average value of peak efficiency and stall margin increases. The flow mechanism of compressor performance improvement after optimization lies in the reduction of a low-velocity separation zone in stator hub region. Moreover, the experiment study confirms the reliability and accuracy of the optimization design method and found that flow instability triggering mode, propagation characteristics of the stall cell, and surge frequency are changed after optimization design. Setting a probability distribution threshold for autocorrelation coefficient and cross-correlation coefficients can be used to predict the arrival of the surge condition.

Role of inertial nonlinearity and coupling stiffness on a series of coupled harvesters

This study investigates the effect of coupling configuration on a state-of-the-art harvesting system consisting of a series of spring-coupled piezoelectric nonlinear monostable tip-loaded cantilever-based vibration energy harvesters (VEHs). The investigation finds favorable coupling configurations that exploit complex nonlinear interaction for improving system performance. The system model incorporates the beam's geometric nonlinearities in the governing equation using inertial and stiffness nonlinear terms. The study first analytically investigates the effect of grounding stiffness on a single harvester's performance. Then, it numerically investigates the effect of increasing array size and changing stiffness distribution. The study discovers a novel hardening/softening transition phenomenon at a critical grounding stiffness value induced by the inertial nonlinear effect. This critical event also maximizes peak output power near the largest eigenfrequency at an optimum grounding stiffness value. At larger arrays, the peak power shows a complex bifurcation behavior, hinting at the presence of multiple attractors. The numerical study of different stiffness configurations and linearized eigenanalysis reveals the necessity of a multilevel optimization strategy focused on both the stiffness ratio between the coupling springs' and their overall stiffness level. This study bridges the gap between previous studies on spring-coupled linear and bistable arrays, offering new insights into coupled nonlinear interactions and proposing novel optimization strategies.

Multi-level optimization strategies for large-scale nonlinear process systems

With growing needs to develop and improve climate-friendly processes, optimization strategies are essential at all levels of decision-making in chemical and energy processes, including process development, process synthesis and design, as well as process operations, control, scheduling, and planning. Challenges include the formulation of well-posed and well-conditioned process models, and development and application of efficient, reliable optimization algorithms. Here we describe a synthesis of optimization concepts and algorithms that enable large-scale nonlinear programming, nonintrusive decomposition strategies and the inclusion of a wide class of surrogate models. All of these are crucial to address challenging nonconvex, multi-scale problems in Computer Aided Process Engineering (CAPE). These elements are demonstrated through dynamic optimization strategies for novel energy generation, demand-based optimization for specialty chemicals, and optimization with integrated heterogeneous models for carbon capture processes.

SERS-based microdroplet platform for high-throughput screening of Escherichia coli strains for the efficient biosynthesis of D-phenyllactic acid.

D-Phenyllactic acid (D-PLA) is a potent antimicrobial typically synthesized through chemical methods. However, due to the complexity and large pollution of these reactions, a simpler and more eco-friendly approach was needed. In this study, a strain for D-PLA biosynthesis was constructed, but the efficiency was restricted by the activity of D-lactate dehydrogenase (DLDH). To address this issue, a DLDH mutant library was constructed and the Surface-Enhanced Raman Spectroscopy (SERS) was employed for the precise quantification of D-PLA at the single-cell level. The TB24 mutant exhibited a significant improvement in D-PLA productivity and a 23.03-fold increase in enzymatic activity, which was attributed to the enhanced hydrogen bonding and increased hydrophobicity within the substrate-binding pocket. By implementing multi-level optimization strategies, including the co-expression of glycerol dehydrogenase (GlyDH) with DLDH, chassis cell replacement, and RBS engineering, a significant increase in D-PLA yields was achieved, reaching 128.4g/L. This study underscores the effectiveness of SERS-based microdroplet high-throughput screening (HTS) in identifying superior mutant enzymes and offers a strategy for large-scale D-PLA biotransformation.

Ranking the risks of eighty pharmaceuticals in surface water of a megacity: A multilevel optimization strategy

Pharmaceuticals in freshwater posed ecological risks to aquatic ecosystem, however, most risk assessments of pharmaceuticals were conducted at screening level, which were limited by the availability of the toxicity data. In this study, risks of 80 pharmaceuticals including 35 antibiotics, 13 antiviral drugs, 13 illicit drugs, and 19 antidepressants in surface water of Beijing were assessed with a proposed multilevel environmental risk optimization strategy. Target pharmaceuticals were detected in surface water samples with the detection frequency from 1.7 % to 100 % and the total concentrations from 31.1 ng/L to 2708 ng/L. Antiviral drugs were the dominant pharmaceuticals. Preliminary screening-level risk assessment indicated that 20 pharmaceuticals posed low to high risks with risk quotient from 0.14 (chloroquine diphosphate) to 27.8 (clarithromycin). Thirteen pharmaceuticals were recognized with low to high risks by an optimized risk assessment method. Of them, the refined probabilistic risk assessment of joint probability curves coupling with a quantitative structure activity relationship-interspecies correlation estimation (QSAR-ICE) model was applied. Clarithromycin, erythromycin and ofloxacin were identified to pose low risks with maximum risk products (RP) of 1.23 %, 0.41 % and 0.35 %, respectively, while 10 pharmaceuticals posed de minimis risks. Structural equation modeling disclosed that human land use and climate conditions influenced the risks of pharmaceuticals by indirectly influencing the concentrations of pharmaceuticals. The results indicated that the multilevel strategy coupling with QSAR–ICE model was appropriate and effective for screening priority pollutants, and the strategy can be used to prioritize pharmaceuticals and other emerging contaminants in the aquatic environment.

Design of a genetically encoded biosensor to establish a high-throughput screening platform for L-cysteine overproduction

Metabolic engineering seeks to rewire the metabolic network of cells for the efficient production of value-added compounds from renewable substrates. However, it remains challenging to evaluate and identify strains with the desired phenotype from the vast rational or random mutagenesis library. One effective approach to resolve this bottleneck is to design an efficient high-throughput screening (HTS) method to rapidly detect and analyze target candidates. L-cysteine is an important sulfur-containing amino acid and has been widely used in agriculture, pharmaceuticals, cosmetics, and food additive industries. However, HTS methods that enable monitoring of L-cysteine levels and screening of the enzyme variants and strains to confer superior L-cysteine biosynthesis remain unavailable, greatly limiting the development of efficient microbial cell factories for L-cysteine production at the industrial scale. Here, we took advantage of the L-cysteine-responsive transcriptional regulator CcdR to develop a genetically encoded biosensor for engineering and screening the L-cysteine overproducer. The in vivo L-cysteine-responsive assays and in vitro electrophoretic mobility shift assay (EMSA) and DNase I footprint analysis indicated that CcdR is a transcriptional activator that specifically interacts with L-cysteine and binds to its regulatory region to induce the expression of target genes. To improve the response performance of the L-cysteine biosensor, multilevel optimization strategies were performed, including regulator engineering by semi-rational design and systematic optimization of the genetic elements by modulating the promoter and RBS combination. As a result, the dynamic range and sensitivity of the biosensor were significantly improved. Using this the excellent L-cysteine biosensor, a HTS platform was established by coupling with fluorescence-activated cell sorting (FACS) and was successfully applied to achieve direct evolution of the key enzyme in the L-cysteine biosynthetic pathway to increase its catalytic performance and to screen the high L-cysteine-producing strains from the random mutagenesis library. These results presented a paradigm of design and optimization of biosensors to dynamically detect metabolite concentrations and provided a promising tool enabling HTS and metabolic regulation to construct L-cysteine hyperproducing strains to satisfy industrial demand.

Design and Optimization of a Fault Tolerant Modular Permanent Magnet Assisted Synchronous Reluctance Motor With Torque Ripple Minimization

This article proposes a new asymmetric modular permanent magnet assisted synchronous reluctance motor (PMaSynRM) to offer low torque ripple and high reliability. First, the stator is segmented into three modules without magnetic coupling. The influence of flux gaps between modules is investigated on torque performance. For the torque improvements, the multilevel optimization strategy is conducted, which simultaneously enhances the fault-tolerant capability. Besides, the torque ripple reduction methods are proposed with the asymmetric rotor and shifted end-slots. The analytical formulae of asymmetric flux barrier angle are derived. Then, the asymmetric flux barrier is designed to reduce the 12th harmonic, and the end-slots are shifted to minimize the additional 2nd harmonic component caused by flux gaps. Finally, the enhanced fault-tolerant capability of the proposed modular PMaSynRM is also evaluated. The effectiveness of the proposed approach is verified by both simulation and experiment.

Multi-Objective Design Optimization of an IPMSM Based on Multilevel Strategy

The multiobjective optimization design of interior permanent magnet synchronous motors (IPMSMs) is a challenge due to the high dimension and huge computation cost of finite element analysis. This article presents a new multilevel optimization strategy for efficient multiobjective optimization of an IPMSM. To determine the multilevel optimization strategy, Pearson correlation coefficient analysis and cross-factor variance analysis techniques are employed to evaluate the correlations of design parameters and optimization objectives. A three-level optimization structure is obtained for the investigated IPMSM based on the analysis results, and different optimization parameters and objectives are assigned to different levels. To improve the optimization efficiency, the Kriging model is employed to approximate the finite element analysis for the multiobjective optimization in each level. It is found that the proposed method can provide optimal design schemes with a better performance, such as smaller torque ripple and lower power loss for the investigated IPMSM, while the needed computation cost is reduced significantly. Finally, experimental results based on a prototype are provided to validate the effectiveness of the proposed optimization method. The proposed method can be applied for the efficient multiobjective optimization of other electrical machines with high dimensions.

Design of High Altitude Propeller Using Multilevel Optimization

An improved D’Angelo optimization framework based on the surrogate model and optimization algorithm is proposed to design and optimize a new high altitude propeller which is applied to the propulsion system of the stratospheric aircraft. The framework adopts a multilevel optimization strategy to determine a high efficiency and light weight propeller. The aerodynamic characteristics of S1223 airfoil are calculated by Computational Fluid Dynamics (CFD) method. The aerodynamic performance of the D’Angelo propeller which is obtained by the first-level optimization is predicted by Blade element momentum (BEM) theory. Then the D’Angelo propeller chord and twist angle, which are regarded as the initial conditions of the second-level optimization, are optimized to achieve the maximum efficiency by Multi-island genetic algorithm (MIGA). In addition, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to maximize the propeller efficiency and minimize the propeller weight. And the Pareto frontier solutions about the efficiency and the blade area are obtained by NSGA-II. What is more, the aerodynamic characteristics of the D’Angelo propeller, the optimization propeller by MIGA, and the optimization propeller by NSGA-II are calculated by CFD simulation and compared with BEM results. It is shown that the CFD results are in fair agreement with BEM results and the aerodynamic performance of the NSGA-II propeller is prior to the MIGA propeller and is close to the D’Angelo propeller. Besides, the NSGA-II propeller is the lightest among them and can satisfy the cruise requirements of the high altitude propeller.

Efficient Design Optimization of Acoustic Liners for Engine Noise Reduction

Engine noise reduction using acoustic liner panels is an established technology in the design of bypass turbofan engines. Using acoustic impedance modeling techniques in aeroacoustic simulations, quick assessment of different liner configurations can be made. To minimize the engine noise in the best possible way, optimal liner configurations are ideally determined by employing numerical optimization methods. In general, to exhaust the full potential of the lining technology, it is desired to use a large number of design parameters in the optimization process. In the present work, a multilevel optimization framework is presented for the design of acoustic liner panels, which can efficiently handle large design vectors. The multilevel method combines a global search strategy with a local gradient-based optimization method. The discrete adjoint solver, which is used to evaluate the liner sensitivities required in the local search, has been developed using algorithmic differentiation techniques. This approach allows consistent treatment of boundary conditions in the adjoint part, thereby increasing the robustness and accuracy of the adjoint solver. In this way, the discrete adjoint solver fully incorporates the features of the underlying aeroacoustic solver, such as the impedance modeling and nonreflecting boundary conditions. The feasibility and the efficiency of the multilevel optimization strategy are demonstrated by finding the optimal liner parameters in a turbofan engine bypass duct configuration.

Planetary landing site detection and selection using multilevel optimization strategy

Reliable landing site detection and selection method is the key to enable a safe planetary landing for both robotic and manned missions. In this paper, a multilevel optimization strategy is newly employed to address this issue. This strategy decomposes the landing site detection and selection problem with constraints into three successive optimization sub-problems, which are then solved as a predetermined sequence in the multilevel structure. In the optimization procedures, an innovation derivatives based hazard detection algorithm is also proposed to solve the first sub-problem. Meanwhile, three performance indexes based on different constraints are defined to locate the optimal landing site. A typical lunar landing site selection scenario is simulated simultaneously using multilevel optimization and existing safety index based optimization, and comparative simulations illustrate the feasibility and superiority of the proposed approach. In addition, robustness assessments of multilevel optimization on initial flight states, guidance laws and landing terrains are also conducted.

A Multi-level Optimization Strategy to Improve the Performance of Stencil Computation

Stencil computation represents an important numerical kernel in scientific computing. Leveraging multi-core or many-core parallelism to optimize such operations represents a major challenge due to both the bandwidth demand and the low arithmetic intensity. The situation is worsened by the complexity of current architectures and the potential impact of various mechanisms (cache memory, vectorization, compilation). In this paper, we describe a multi-level optimization strategy that combines manual vectorization, space tiling and stencil composition. A major effort of this study is to compare our results with the Pochoir framework. We evaluate our methodology with a set of three different compilers (Intel, Clang and GCC) on two recent generations of Intel multi-core platforms. Our results show a good match with the theoretical performance models (i.e. roofline models). We also outperform Pochoir performance by a factor of x2.5 in the best case.

Architecture, Languages, Compilation and Hardware support for Emerging ManYcore systems (ALCHEMY): Preface

Manycore systems are one of the key enabler technologies for most of current computational paradigms, including Internet of things, data-centers on chip and big data processing. These paradigms are characterized by tight and demanding requirements such as code portability, dynamicity, high performance, usability, predictability, reliability, low power and security.This combination of requirements has led to heterogeneous manycore systems which are extremely challenging to design and to program. As a result, a large body of research has focused on development of languages, simulation environments and analysis tools that allow to model and predict the behavior of this type of systems from early design stages.ALCHEMY 2017 presents five research works addressing the challenges of code portability, high performance, usability, security and reliability in manycore systems. These are namely:1. ”An OpenMP backend for the Sigma-C streaming language, addresses the software portability challenge of manycore architectures. It proposes an implementation of an OpenMP backend for the SigmaC language, a cycle-static data flow abstraction to program many-core embedded platforms. Its compilation scheme allows for utilization of future manycore embedded systems such as Kalray’s MPPA.2. A multi-level optimization strategy to improve the performance of the stencil computation combines manual vectorization, space tiling and stencil composition for achieving high performance of stencil kernels on manycore systems. The evaluation with three compilers (Intel, Clang and GCC) and two target multi-core platforms (Intel Broadwell and Ivybridge) reports better results compared to the state of the art.3. A Distributed Shared Memory Model and C++ Templated Meta-Programming Interface for the Epiphany RISC Array Processor addresses the usability challenge. It proposes techniques for data layout and parallel loop order abstraction as a parallel programming API targeting the Epiphany architecture. This results into a transparent distributed shared memory (DSM) model for Epiphany that eliminates the need to manage local data movement between cores.4. Towards Protected MPSoC Communication for Information Protection against a Malicious NoC deals with vulnerabilities on Network-on-Chip (NoC). The authors propose a security protocol which allows the secure communication among the cores of the system, even in the presence of Trojan insertions at the NoC whose aim is to modify and steal data.5. GPU-Accelerated Real-Time Path Planning and the Predictable Execution Model addresses the reliability challenge and tackles the important problem of ensuring reliable Worst Case Execution Time for Real-Time and Cyber Physical Systems. While considering heterogeneous (CPU/GPU), the idea is to separate memory and processor operations through Time-Division Multiplexing (TDM).

Space–time adaptive solution of inverse problems with the discrete adjoint method

This paper develops a framework for the construction and analysis of discrete adjoint sensitivities in the context of time dependent, adaptive grid, adaptive step models. Discrete adjoints are attractive in practice since they can be generated with low effort using automatic differentiation. However, this approach brings several important challenges. The space–time adjoint of the forward numerical scheme may be inconsistent with the continuous adjoint equations. A reduction in accuracy of the discrete adjoint sensitivities may appear due to the inter-grid transfer operators. Moreover, the optimization algorithm may need to accommodate state and gradient vectors whose dimensions change between iterations. This work shows that several of these potential issues can be avoided through a multi-level optimization strategy using discontinuous Galerkin (DG) hp-adaptive discretizations paired with Runge–Kutta (RK) time integration. We extend the concept of dual (adjoint) consistency to space–time RK-DG discretizations, which are then shown to be well suited for the adaptive solution of time-dependent inverse problems. Furthermore, we prove that DG mesh transfer operators on general meshes are also dual consistent. This allows the simultaneous derivation of the discrete adjoint for both the numerical solver and the mesh transfer logic with an automatic code generation mechanism such as algorithmic differentiation (AD), potentially speeding up development of large-scale simulation codes. The theoretical analysis is supported by numerical results reported for a two-dimensional non-stationary inverse problem.

Adjoint-based optimization of multi-phase flow through porous media – A review

The recovery of oil from subsurface reservoirs often requires the injection of water or gas to maintain reservoir pressure and to displace the oil from injection to production wells. The design of an economically optimal recovery strategy is usually based on ’reservoir simulation’, i.e. large-scale numerical simulation of the flow of multi-phase fluids through strongly heterogeneous porous media with uncertain coefficients. Control of the recovery process is through prescribing time-varying pressures or flow rates in the wells. Efficient methods to optimize the recovery strategy make use of gradients of an economic objective function with respect to the well controls at every time step. These can be obtained efficiently with the aid of adjoint-based techniques. Constraints, in particular those that involve states (reservoir pressures or saturations) or outputs (measured well pressures or rates) require special treatment. Uncertainty in the coefficients can be incorporated through robust optimization over an ensemble of models. The limited controllability of the reservoir states offers scope for reduced-order modeling using techniques like proper orthogonal decomposition. ‘Closed-loop’ optimization can be performed through frequent repetition of the optimization during the producing life of the field in combination with updating the of the model coefficients based on production measurements. Moreover, an emerging technology is the operational use of model-based optimization which requires a combination of long-term and short-term objectives through multi-level optimization strategies.

Gradient based optimization of semi-numerical models with symbolic sensitivity: Application to a simple ferromagnetic MEMS switch device

This paper deals with electromagnetic modeling dedicated to constrained optimization needs. A tool dedicated to magnetic devices with simple shapes is presented and applied to the design of magnetic MEMS. This tool allows semi-numerical modeling and provides symbolic gradient computation. A multi-level optimization strategy is used to ensure fast and global convergence of magnetic latch micro device.

Efficient solution of a three-dimensional inverse heat conduction problem in pool boiling

In this paper, we consider a three-dimensional transient inverse heat conduction problem arising in pool boiling experiments, i.e., the reconstruction of the surface heat flux from pointwise temperature observations inside a heater. We show that the inverse problem is ill-posed and utilize Tikhonov regularization and conjugate gradient methods together with a discrepancy stopping rule for a stable solution. We investigate the proper choice of regularization terms, which not only affects stability of the reconstructions but also greatly influences the quality of reconstructions in the case of limited observations. For the numerical solution of the governing partial differential equation, a space-time finite element method is used. This allows us to compute exact gradients for the discretized Tikhonov functional, and enables the use of conjugate gradient methods for the solution of the regularized inverse problem. We discuss further aspects of an efficient implementation, including a multilevel optimization strategy, together with an implementable stopping criterion. Finally, the proposed algorithms are applied to the reconstruction of local boiling heat fluxes from experimental data.

Adaptive manifold‐mapping using multiquadric interpolation applied to linear actuator design

PurposeThis work aims to present a multilevel optimization strategy based on manifold‐mapping combined with multiquadric interpolation for the coarse model construction.Design/methodology/approachIn the proposed approach the coarse model is obtained by interpolating the fine model using multiquadrics in a small number of points. As the algorithm iterates the response surface model is improved by enriching the set of interpolation points.FindingsThis approach allows to accurately solve the TEAM Workshop Problem 25 using as little as 33 finite element simulations. Furthermore, it allows a robust sizing optimization of a cylindrical voice‐coil actuator with seven design variables.Research limitations/implicationsFurther analysis is required to gain a better understanding of the role that the initial coarse model accuracy plays in the convergence of the algorithm. The proposed model allows to carry out such analysis by varying the number of points included in the initial response surface model. The effect of the trust‐region stabilization in the presence of manifolds of equivalent solutions is also a topic of further investigations.Originality/valueUnlike the closely related space‐mapping algorithm, the manifold‐mapping algorithm is guaranteed to converge to a fine model optimal solution. By combining it with multiquadric response surface models, its applicability is extended to problems for which other kinds of coarse model such as lumped parameter approximations for instance are tedious or impossible to construct.

An optimization strategy for nonlinear simulated moving bed chromatography: Multi-level optimization procedure (MLOP)

This paper presents a multi-level optimization strategy to obtain optimum operating conditions (four flow-rates and cycle time) of nonlinear simulated moving bed chromatography. The multi-level optimization procedure (MLOP) approaches systematically from initialization to optimization with two objective functions (productivity and desorbent consumption), employing the standing wave analysis, the true moving bed (TMB) model and the simulated moving bed (SMB) model. The procedure is constructed on a non-worse solution property advancing level by level and its solution does not mean a global optimum. That is, the lower desorbent consumption under the higher productivity is successively obtained on the basis of the SMB model, as the two SMB-model optimizations are repeated by using a standard SQP (successive quadratic programming) algorithm. This approach takes advantage of the TMB model as well as surmounts shortcomings of the TMB model in the general case of any nonlinear adsorption isotherm using the SMB model. The MLOP is evaluated on two nonlinear SMB cases characterized by i) quasi-linear/non-equilibrium and ii) nonlinear/nonequilibrium model. For the two cases, the MLOP yields a satisfactory solution for high productivity and low desorbent consumption within required purities.

Component mode synthesis-based design optimization method for local structural modification

This research integrates component mode synthesis with a multilevel design optimization strategy to develop a design methodology for making the dynamical modification of complex structures simpler and tractable. The component mode synthesis formulates the eigenvalue equation of the entire structure in terms of vibration characteristics of local structure components. With this particular feature, the component mode synthesis helps to facilitate a two-stage procedure for the dynamic modification of a complex structure; the lower-level design optimization modifies the local structure components whose performances are prescribed by the optimal solution of the upper-level design optimization. The paper first discusses the formulation and the derivation of the Kuhn-Tucker necessary conditions for the proposed design optimization procedure and then presents numerical examples to demonstrate its numerical implementation and applicability.