Latin Hypercube Design Of Experiments Research Articles

Gas exchange optimization in aircraft engines using sustainable aviation fuel: A design of experiment and genetic algorithm approach

The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93°CA at sea level and 82°CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.

Uncertainty Study Aids Development-Plan Optimization in Deepwater Gulf of Mexico

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32622, “Development-Plan Optimization and Uncertainty Study in a Major Deepwater Field in the US Gulf of Mexico,” by Mohsen Rezaveisi, SPE, Jennifer L. Campbell, and Paula L. Wigley, Woodside Energy Group, et al. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. _ This paper describes development-plan optimization and a probabilistic uncertainty study using Latin hypercube experimental design constrained to production performance in Lower Miocene (LM) reservoirs of the deepwater Gulf of Mexico Shenzi Field. The purpose of the development-plan optimization was to identify, rank, and characterize future development opportunities (i.e., infills and injectors) in the LM reservoirs to arrest field decline. The study uses history-matched dynamic simulation models. Introduction The Shenzi discovery is approximately 122 miles off the coast of Louisiana in a water column approximately 4,400 ft deep (Fig. 1). This part of the field is a partially filled three-way closure against a salt-cored anticline. The discovery includes four main sand units (A, B, C, and D) that exhibit various degrees of hydraulic connectivity based on pressure data and production history. The area of interest in the current study is the Eastern part of the field. Static Model Build The geomodel covering the area of interest was built from two seismically interpreted horizons and approximately 180 faults. The model is subdivided into 13 zones using well-based true stratigraphic thickness maps. The average cell height is 2–3.5 ft thick with heterolithics driving the lower end of the range. Sequential indicator simulation was used to populate facies using well-based net sand trend maps by zone. In zones with evidence of channelization, a second nested facies model was used to build channels into the model. Shenzi East reservoir properties are good because of the high-quality sand deposited in the field. Lower reservoirs (C and D) have slightly more depositional complexity because of the varying paleotopography and a lower average net-to-gross (NTG) of approximately 53%. Upper reservoirs (A and B) had a larger sediment supply and were more continuously deposited, which resulted in a larger average NTG of approximately 67%. Dynamic Model History Matching The Shenzi East reservoir has been on production since March 2009, providing abundant production data for history matching. Of seven producers, six are still active, and the reservoir also features three water injectors. Producers and injectors are identified with letters P and I, respectively, followed by well numbers. Well P7 was drilled after 6 months of production from other producers; formation pressure data showing reservoir depletion are available for history matching. The water injectors were drilled in 2012–2013, but only two have formation pressure testing data available. Well P1 was completed in all sands; Wells P2, P3, and P6 were completed in three sands (D, C, and B); and the rest of the producers were completed in two sands (D and C). Production is commingled in all producers, resulting in allocation uncertainties between sands. Water production has been observed in five wells, with the current water cut in the range of 35–65%. The two most updip wells, P1 and P7, are still producing at zero water cut.

A design and analysis of computer experiments based mixed integer linear programming approach for optimizing a system of electric vehicle charging stations

This paper formulates a mixed integer linear programming (MILP) model to optimize a system of electric vehicle (EV) charging stations. Our methodology introduces a two-stage framework that integrates the first-stage system design problem with a second-stage control problem of the EV charging stations and develops a design and analysis of computer experiments (DACE) based system design optimization solution method. Our DACE approach generates a metamodel to predict revenue from the control problem using multivariate adaptive regression splines (MARS), fit over a binned Latin hypercube (LH) experimental design. Comparing the DACE based approach to using a commercial solver on the MILP, it yields near optimal solutions, provides interpretable profit functions, and significantly reduces computational time for practical application.

Effect of multi-parameter optimization of water-laser coupling device and nozzle geometry on the stability of water-guided laser beam

The stability of the water jet flow used in water-guided laser processing plays a crucial role in determining the quality of the processing process. The present study aims to investigate the effect of water-laser coupling device and nozzle geometry on the stable length of a water-guided laser beam. A numerical model is developed to analyze the internal flow field within the coupling device. Computational fluid dynamics (CFD) simulations are employed to examine how variations in cone nozzle's length-to-diameter ratio, divergence angle, nozzle aperture, and inlet pressure that affect the flow characteristics of water jet. Subsequently, the Latin Hypercube experimental design method is employed to establish parameter samples and construct a Kriging approximation model for the stable length of water-guided laser beam. The Multiple Island Genetic Algorithm (MIGA) is utilized for global optimization of the approximation model, while CFD methods are employed to analyze and validate the optimization results. Finally, experimental verification was conducted to determine the stable length of water-guided laser beam generated by the optimized cone nozzle structure. The research findings demonstrate that the optimized nozzle structure can compensate for approximately 21 mm in the stable distance of water-guided laser beam under the pressure of 5.0 MPa. This study provides valuable guidance for enhancing the performance and engineering applications of laser micro-jet processing technology.

Data-driven modelling and evaluation of a battery-pack system’s mechanical safety against bottom cone impact

The exploration of the mechanical responses of the battery pack system (BPS) when subjected to dynamic impact loads is crucial for the safety of power batteries during collisions. To elucidate the mechanical response mechanism of the BPS under bottom impact, in this study, a series of finite element (FE) analysis tests were conducted in simulation environments with various combinations of the top radius of the cone model simulating objects, shell thickness, impact velocity, and impact angle. In the FE simulation, the BPS’s nonlinear model was developed and then validated through modal analysis. Subsequently, five machine learning (ML) models enhanced by optimized algorithms were used to evaluate the mechanical response to the bottom impact of BPS. The training data of the ML model is obtained through the optimal Latin hypercube experimental design based on the FE dynamic simulation process. By comparison between cases, the predictive capabilities of various ML models are examined and the robustness of these models are assessed through adding Gaussian noise to ML models. In conclusion, the results demonstrate that ML methods can effectively evaluate the mechanical response of BPS bottom impact and furnish critical design inputs for manufacturing next-generation batteries with durability and safety limitations.

Research on a Multi-Objective Optimization Design for the Durability of High-Performance Fiber-Reinforced Concrete Based on a Hybrid Algorithm

To achieve durable high-performance fiber-reinforced concrete that meets economic requirements, this paper introduces a hybrid intelligent framework based on the Latin hypercube experimental design, response surface methodology (RSM), and the NSGA-III algorithm for optimizing the mix design of high-performance fiber-reinforced concrete. The developed framework allows for the prediction of concrete performance and obtains a series of Pareto optimal solutions through multi-objective optimization, ultimately identifying the best mix proportion. The decision variables in this optimization are the proportions of various materials in the concrete mix, with concrete’s frost resistance, chloride ion permeability resistance, and cost as the objectives. The feasibility of this framework was subsequently validated. The results indicate the following: (1) The RSM model exhibits a high level of predictive accuracy, with coefficient of determination (R-squared) values of 0.9657 for concrete frost resistance and 0.9803 for chloride ion permeability resistance. The RSM model can be employed to construct the fitness function for the optimization algorithm, enhancing the efficiency of multi-objective optimization. (2) The NSGA-III algorithm effectively balances durability and cost considerations to determine the optimal mix proportion for the concrete. After multi-objective optimization, the chloride ion permeability resistance and frost resistance of the high-performance fiber-reinforced concrete improved by 38.1% and 6.45%, respectively, compared to the experimental averages, while the cost decreased by 2.53%. The multi-objective optimization method proposed in this paper can be applied to mix design for practical engineering projects, improving the efficiency of concrete mix design.

Surrogate-based optimization on bump for shock wave/boundary layer interaction control

Supersonic and hypersonic vehicles have complex shock wave/boundary layer interaction problems during flight, and it adversely affect the aerodynamic performance of the vehicle. In this paper, the bump passive control approach is introduced to control the shock wave/boundary layer interaction, and its configuration is optimized with the minimum separation area and the maximum total pressure recovery coefficient. The Kriging model is established by means of the Latin hypercube experimental design approach to obtain the sample points. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm is utilized to optimize the parameters of the bump configuration, and the flow field structures of the uncontrolled condition, the special basic condition and the optimized condition are compared and analyzed. The obtained results show that the surrogate-based optimized model shows better performances than the basic condition. In the parameter setting range considered in this study, the optimized Case 3c with the middle position in the optimization front owns a larger total pressure recovery coefficient and a smaller separation area, with its separation area and total pressure recovery coefficient being 0.80 mm2 and 67.39% respectively.

Optimization of aerodynamic drag reduction for vehicles with non-smooth surfaces and research on aerodynamic characteristics under crosswind

Energy consumption and environmental pollution problems have become increasingly prominent in China. The drag reduction of vehicles is an effective method to improve the fuel economy of cars and reduce greenhouse emissions. In this paper, based on the MIRA squareback model, the influence of the shapes, arrangements, layout positions, and dimensions of the non-smooth units on the aerodynamic characteristics of vehicles are investigated through the computational fluid dynamics (CFD) method. The depth, longitudinal spacing, and horizontal spacing are taken as the design variables and the aerodynamic drag coefficient is used as the optimization objective. The optimal Latin hypercube experimental design method is adopted to select sample points, and the response values of each experiment sample point are calculated through the integrated platform in Isight software. According to the response values, the Kriging approximation model is built and validated. Furthermore, the multi-island genetic algorithm is applied to optimize the dimensions of pit non-smooth units. The aerodynamic characteristics of the smooth surface model and the optimized non-smooth surface model under steady crosswind are conducted using numerical simulation. The findings obtained from the investigation will provide a certain reference for the application of non-smooth units in the field of automobiles.

Genetic optimization design for aerodynamic structure of oblique flow compressor radial inlet chamber in compressed air energy storage system

AbstractThe compressor plays a significant role in the compressed air energy storage (CAES) system, and its performance directly determines the overall efficiency of the system and the economy of energy storage. Internal flow losses and outlet distortion due to poor design of its radial inlet chamber (RIC) geometry can degrade the performance of downstream components and reduce compressor unit performance. In this article, a multiobjective optimization design is carried out for the RIC of the oblique flow compressor in CAES system, and a parametric optimization design method of the RIC annular convergent channel meridian profile is developed. The optimal Latin hypercube experimental design (opt LHD) method is used to generate the sample library, and the radial basis function neural network (RBFNN) is used to establish the surrogate model of each design parameter and RIC performance. The multiobjective optimization is executed by the second‐generation non‐dominated sorting genetic algorithm (NSGA‐II). After optimization, the area of the annular convergent channel of the RIC is expanded, the transition of the meridian profile is smoother, the distortion coefficient of the RIC is reduced by 0.58%, and the total pressure loss coefficient is reduced by 16.45%. By using the optimized RIC, the performance of oblique flow compressor has been improved. Compared with the original model, the total pressure ratio and isentropic efficiency of the whole machine at the design point have been increased by 0.6% and 0.32%, respectively.

6σ robust and optimal design of micro-motion platform based on kriging model

The two performance indexes of output displacement and inherent frequency are closely related to the design variables of the micro-motion platform. To address the problem that the design variables have certain fluctuations and the high nonlinearity between the design variables and the performance of the micro-motion platform, a multi-objective robust optimization method with high computational efficiency and high fitting accuracy is proposed. Based on the kriging agent model, the high-precision approximate functions of the key design variables with the output displacement and the inherent frequency are established by the Latin hypercube experimental design, respectively. With the maximum stress as the constraint, Monte Carlo simulation, 6σ robust design concept, and nondominated sorting genetic algorithm II (NSGA-II) multi-objective genetic algorithm are integrated to optimize the structure of the micro-motorized platform. The results show that compared with the traditional deterministic optimization method, the 6σ robust design considering parameter uncertainty effectively reduces the fluctuation of the performance of the micromotion platform and improves the resistance of the platform performance to disturbance under uncertainty.

Multi-Objective Optimization of Rear Guide Vane of Diagonal Flow Fan Based on Robustness Design Theory

As an essential fluid machine in national production, the optimal design of the performance of fans has long been a focus of research. In the format of small diagonal flow fans with rear guide vanes, there are too many design variables, and the design parameters interact with each other, resulting in a degradation of the fan’s performance under actual operating conditions. Under realistic conditions, many uncertainties exist in the object of study and fluctuations in variables and parameters lead to a specific deviation of the optimization target from the design target. A method is needed for the robust and optimal design of angular flow fans under stochastic operating conditions of the design variables. In this paper, a multi-objective aerodynamic performance powerful design method is proposed using Pearson correlation analysis, combined with CFD calculation method, Latin hypercube experimental design and the Kriging agent model, to optimize the rear guide vane structural parameters of the diagonal flow fan under uncertain aerodynamic performance conditions, analyze the flow field characteristics before and after the modification, and select the optimal design model for the release. The test results show that the total pressure of the optimized fan is increased by 86 Pa, and the noise is reduced by 2.4 dB. The proposed optimization method is effective and can also be used to optimize the performance of other types of fans.

Protection challenges in seat positions with large rearward adjustment in frontal collisions: An approach using stochastic human body model simulations

Novel seat positions enabled by self-driving cars have been investigated in various studies in recent years. However, there is little research on the effect of increased rearward seat adjustments. To predict challenges associated with the possibility to move the seat further backwards in the car than currently possible as driver, appropriate methods have to be defined. A detailed human body model, a THUMS v4.1 in particular, tissue-based injury risk evaluation methods, a generic vehicle interior and a Latin hypercube design of experiments taking the variability of real-world crashes into account was established. In a first step, 200 simulations at current representative seat positions and a driving occupant posture were performed. The results were then compared to field data from an accident database to evaluate the accuracy of the method. The predictions exceeded the injury risks for the abdomen, head, and upper extremities, while underestimating the risk for thoracic and lower extremity injuries. A good match was observed for injuries of the neck and spine. In a second step, the 200 simulations were run again, but with the seat adjusted rearwards significantly. In this seat configuration, with the exception of the head and the upper extremities, increased injury risks were predicted for all body regions. The highest increases affected the lower extremities (+28%)—predominantly pelvic fractures—and the neck (+9%). In addition, (partial) submarining occurred in almost 50% of cases with the rearward adjusted seat—as opposed to none in the conventional seat position. The established method could be used in future studies to design safety measures addressing these identified potential safety risks.

Estimating economic losses from cyber-attacks on shipping ports: An optimization-based approach

The Maritime Transportation System (MTS) accounts for more than 80% of global merchandise trade in volume and roughly one-sixth of the Total Gross Output of the United States. Given that national and global economies depend upon efficient supply chains, port stakeholders must develop security plans to respond to all hazards, natural and manmade. Given recent cyber-attacks affecting shipping ports, along with the multi-billion dollar cyber insurance gap, ports need to understand the tradeoffs between increased competitiveness and higher risk through investment in automation and advanced logistics technologies. This article addresses the need to understand the economic impact of cyber-attacks that affect shipping port operations and thereby enable risk assessments that holistically evaluate interactions among port Information Technology (IT) and Operational Technology (OT) systems. Using a Nearly-Orthogonal Latin Hypercube (NOLH) experimental design, we construct transportation disruption profiles based on actual cyber-attacks that specify the range of operational effects of IT/OT dependencies on stakeholder transportation assets. To capture the costs of the physical disruption, we extend Boland et al’s Dynamic Discretization Discovery (DDD) algorithm to capture capacity constraints and enable delay modeling to accommodate commodities arriving late due to disruption. Economic loss functions for seven commodity categories based on the willingness to pay literature are used to compute delay costs so that stakeholders can estimate the range of economic and operational impacts within a disruption profile. Results based on data for cyber-attacks on landlord port and terminal operator assets provided by Port Everglades, FL illustrate impacts at $80,000 and $1.2M on average during one week in October 2017 and at $141,000 and $2.8M for May 2017 respectively. The runtime performance of our enhanced DDD algorithm improves on the state of the art by an order of magnitude and on larger problem sizes based on real-world port networks.

Multi-Objective Optimization of an Axial Flow Turbine Design Using Surrogate Modeling and Genetic Algorithm

Abstract A surrogate model-based multi-objective design optimization methodology using a nondominated sorting genetic algorithm is used to maximize the power output and efficiency of an axial flow turbine. A set of high-fidelity data obtained from ANSYS-CFD simulations on space-filling samples is used for developing a surrogate model. The methodology uses (i) a Latin hypercube experimental design for selecting space-filling samples, (ii) a genetic algorithm for determining parameters of a Kriging model, and (iii) axial gap, tip clearance, and rotation angle of nozzle profile of turbine as predictor variables. Flow in the turbine is characterized by the presence of jet and wake flow, shock in the rotor blade flow passage, and tip clearance vortex. Study shows that (i) the axial gap controls the mixing of the jet and the wake flows and provides proper blade incidence which reduces the shock strength in the rotor blade flow passages and (ii) an optimum sized tip clearance vortex controls tip clearance leakage. The optimization provided a set of Pareto-optimal solutions that are nondominated in terms of power and efficiency. Verification of a selected design configuration from the Pareto-optimal solution using computational fluid dynamics (CFD) analysis showed that the process of optimization has been able to fine-tune the axial gap and the tip clearance.

Investigation of effects of tire contour on aerodynamic characteristics and its optimization

Reducing the aerodynamic drag is one of the most important approaches for the development of energy-saving and environment-friendly automobiles. The tire contour has a great influence on the aerodynamic characteristics of automobiles. The aim of this study is to investigate the influence of the tire contour design parameters on the aerodynamic characteristics around a closed wheel, and obtain the optimized tire contour to reduce the automobile aerodynamic drag. A passenger car tire 185/65R14 was selected to conduct the wind tunnel test, and the surface pressure coefficients were used to validate the simulation model established using the detached eddy simulation (DES) model. To decrease tire drag, and taking the upper sidewall height, the tread radii, the tread width, and the transition arc radius of the shoulder as four design variables of contour, a combination of the Latin hypercube experimental design, the Kriging surrogate model, and the adaptive simulated annealing (ASA) algorithm were used to optimize the tire contour design parameters. The changes of flow field around the tire, including the velocity, turbulent kinetic energy, and pressure field were compared and analyzed for further understanding of the drag reduction mechanism. It is found that the aerodynamic drag coefficient of the optimized tire is reduced by 14.5%, and the aerodynamic coefficient drag of the car using the optimized tire is reduced by 7%. The present results are expected to provide useful information for designing new tire structures and improving the aerodynamic performance of the automobile.

Lightweight robust optimization design of mechanical claws of intelligent sanitation vehicle

As the dustbin grabbing device of the intelligent sanitation vehicle, the dimension errors caused by the manufacturing process should be considered in the lightweight design process of the mechanical claws. In this study, we used the mechanical claws as the carrier. The maximum deformation and the maximum equivalent stress of them were taken as the design responses and the plate thicknesses of different parts were taken as the design variables. A light-weight robust optimization design method for the mechanical claws was proposed by combining the optimal latin hypercube experimental design with high-precision Kriging approximate model and using Particle Swarm optimization (PSO) as the optimization solver. The optimization results show that this method has higher robustness than traditional deterministic methods although it has lower lightweight requirements. The final structural design of this study has higher advantages in preventing the fluctuation of uncertain parameters.

Estimating Economic Losses from Cyber-Attacks on Shipping Ports: An Optimization-Based Approach

The Maritime Transportation System (MTS) accounts for more than 80% of global merchandise trade in volume and roughly a sixth of the Total Gross Output of the United States. Given that national and global economies depend upon efficient supply chains, port stakeholders must develop security plans to respond to all hazards, natural and manmade. Given recent cyber attacks affecting shipping ports, along with the multi-billion dollar cyber insurance gap, ports need to understand the tradeoffs between increased competitiveness and increased risk through investment in automation and advanced logistics technologies. This article addresses the need to understand the economic impact of cyber attacks that affect shipping port operations and thereby enable risk assessments that holistically evaluate interactions among port Information Technology (IT) and Operational Technology (OT) systems. We extend Boland et al's Dynamic Discretization Discovery (DDD) algorithm to include capacity constraints and delay arcs to accommodate commodities arriving late due to disruption. Using a Nearly-Orthogonal Latin Hypercube (NOLH) experimental design, we construct disruption profiles based on actual cyber attacks that specify the range of operational effects of IT/OT dependencies on stakeholder transportation assets. Economic loss functions for seven commodity categories based on the willingness to pay literature are used to compute delay costs so that stakeholders can estimate the range of economic and operational impacts within a disruption profile. Results based on data provided by Port Everglades, FL, illustrate impacts at $80,000 and $1.2M on average for cyberattacks on landlord port and terminal operator assets during the first week in October, 2017. The runtime performance of our DDD algorithm improves on the state of the art by an order of magnitude and on larger problem sizes based on real-world port networks.

Systems dynamics and the uncertainties of diagnostics, testing and contact tracing for COVID-19.

The current COVID-19 pandemic contains an unprecedented amount of uncertainty and variability and thus, there is a critical need for understanding of the variation documented in the biological, policy, sociological, and infrastructure responses during an epidemic to support decisions at all levels. With the significant asymptomatic spread of the virus and without an immediate vaccine and pharmaceuticals available, the best feasible strategies for testing and diagnostics, contact tracing, and quarantine need to be optimized. With potentially high false negative test results, infected people would not be enrolled in contact-trace programs and thus, may not be quarantined. Similarly, without broad testing, asymptomatic people are not identified and quarantined. Interconnected system dynamics models can be used to optimize strategies for mitigations for decision support during a pandemic. We use a systems dynamics epidemiology model along with other interconnected system models within public health including hospitals, intensive care units, masks, contact tracing, social distancing, and a newly developed testing and diagnostics model to investigate the uncertainties with testing and to optimize strategies for detecting and diagnosing infected people. Using an orthogonal array Latin Hypercube experimental design, we ran 54 simulations each for two scenarios of 10% and 30% asymptomatic people, varying important inputs for testing and social distancing. Systems dynamics modeling, coupled with computer experimental design and statistical analysis can provide rapid and quantitative results for decision support. Our results show that widespread testing, contacting tracing and quarantine can curtail the pandemic through identifying asymptomatic people in the population.

Innovative time efficient method to optimize buildings’ performance using Design of Experiment, Polynomial Regression and Genetic Algorithms

Dynamic thermal simulation use is essential for studying complex building systems. One of the major challenges to widely deploy optimization in this field’ engineering and research is the high computational cost to perform the required simulation-based iterations. To participate in overcoming this hindrance, we propose in this paper an innovative time efficient Multiobjective Optimization methodology, based on coupling Latin Hypercube Design of Experiments to Artificial Neural Network polynomial regression and Genetic Algorithms (GA). This method allows to benefit from the robustness of Genetic Algorithms and from the rapidity of prediction of polynomial regression. RBD-FAST sensitivity Analysis indexes are also generated without any extra-time. We successfully applied the method to analyse and optimize a constrained 8-objectives problem with 13 input parameters in six climatic zones in Morocco.

Optimization design of heat pipe heat exchanger using response surface methodology

In this paper, Optimization design of heat pipe heat exchanger (HPHX) is processed utilizing the Response Surface Methodology (RSM). The response surface model was built by regressive analysis using Latin hypercube experimental design method and numerical simulation. Through response surface analysis, it is found that the two input variables affecting the performance of HPHX are the heat pipe pitch and the Inlet and outlet distance. Moreover, the maximum value of the overall performance factor on the response surface is searched using genetic algorithm, and the optimal values of four input variables are obtained.