Design of experiments in thermal architecture

An attractive feature of many simulation packages is their availability on desktop computers and their potential for allowing the user to run a simulation model under different conditions in a highly interactive way. Such a way of studying a system is attractive because of its immediacy and the direct control it offers the user. Design of Experiments is a statistical technique for quickly optimizing performance of systems. It starts with a screening experimental design test plan involving all of the known factors that are suspected to affect the system’s performance (or output). When the number of input variables or test factors is large, the primary experimental objective is to pare this number down into a manageable few. This is usually followed by another designed experiment design or test plan with the objective of optimizing the system’s performance. For an easy and interactive use of the design of experiments technique, a new tool called DOET (which stands for ‘Design Of Experiments Tool’) has been developed. This paper aims to illustrate the design of experiments technique using the DOET.In this paper we consider the implementation of the methodology of the design of simulation experiments in the form of interactive tool attached to the simulation model called DOET (which stands for ‘Design Of Experiments Tool’). The DOET receives the output from the simulation model and automatically processes this output using the DOE technique. The DOET thus can be used interactively either to give advice to the user conducting the simulation study, or else to directly control the conduct of the runs.

The production of high yields of soluble recombinant protein is one of the main objectives of protein biotechnology. Several factors, such as expression system, vector, host, media composition and induction conditions can influence recombinant protein yield. Identifying the most important factors for optimum protein expression may involve significant investment of time and considerable cost. To address this problem, statistical models such as Design of Experiments (DoE) have been used to optimise recombinant protein production. This review examines the application of DoE in the production of recombinant proteins in prokaryotic expression systems with specific emphasis on media composition and culture conditions. The review examines the most commonly used DoE screening and optimisation designs. It provides examples of DoE applied to optimisation of media and culture conditions.

(1996). Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. Journal of Quality Technology: Vol. 28, No. 4, pp. 483-484.

Optimization of MBR hydrodynamics for cake layer fouling control through CFD simulation and RSM design

The ergonomic design of control elements in real life has been researched in detail. Various studies exist on the optimal dimensioning, their haptic and acoustic feedback to achieve high control accuracy and user experience. But the development of products is increasingly done with virtual prototypes. Virtual reality (VR) allows these prototypes to be tested in a highly immersive environment. However, the findings from reality cannot be transferred to VR directly. For example, users in VR interact with the prototypes using controllers, which affects haptic feedback. This study investigates how rotary dials and joysticks must be designed and programmed in VR so that control tasks can be performed efficiently and generate a high user experience and perceived presence.In user tests, subjects (n = 25) evaluate the control of a joystick and a rotary dial in VR. In a virtual crane operator's cabin or at a virtual table, the subjects (f = 10, m = 15, age: 24 +- 3) perform four predefined tasks per control element. On two screens in VR, subjects see a vertical bar graph with a scale from 0 to 100 % controlled by the joystick and separately a numerical value between 0 and 100 % which is controlled by the rotary dial. The screens display the task to the subjects, e.g., "Set the value from 0 % to 42 %". According to the method “design of experiments”, 14 factors, such as vibration feedback, acoustic feedback, position of the subject or the sensitivity of the control element are systematically varied on two respectively three levels (e.g., diameter of actuator 40 mm, 80 mm or 110 mm). For each trial, the control accuracy and the time required to complete the task are determined. In addition, the perceived presence is assessed using the Slater-Usoh-Steed-Questionnaire and the user experience is surveyed using the User Experience Questionnaire. The effect of a change of level on the response parameters is investigated using multifactorial ANOVA (α = .05). Linear regression is used to calculate a mathematical relationship between factor and response parameter. These mathematical models are used to calculate which factor values can be used to achieve a high level of control accuracy with a low time requirement and a high level of user experience and perceived presence. The factors angular resolution, inclination, shape of the rotary dial and position of the subject have a highly significant effect (p ≤ .001) on the time required to complete the tasks with the rotary dial. On the control accuracy of the rotary dial, the angular resolution, the VR-controller and the interaction of angular resolution and diameter of the rotary dial have a significant effect. On the user experience, a total of six factors and two interactions have a significant effect. On the perceived presence of the subjects, the VR environment and the diameter of the rotary dial have a significant effect. The calculated optimized design is a rotary dial with vibration feedback, without acoustic feedback, with visualization of a rough knurling, an angular resolution of 10-12 degree/value, a 40 mm diameter and no inclination. Visualization of the hand should be avoided.Sensitivity, size, subject position, VR environment, and the interaction of subject position and VR environment have a significant effect on the time required to perform the control tasks with the joystick. Three factors and one interaction have a significant effect on the control accuracy of the joystick. The interaction of the factors vibration feedback and visualization of the hand has a significant effect on the perceived presence of the subjects. On user experience, nine factors and five interactions have a significant effect. The calculated optimized levels of factors for the joystick are vibration and acoustic feedback, no visual feedback, vertical handle with a height of 20-24 cm, a five-level angular resolution, a maximum deflection angle of +- 15°, a sensitivity of 8 %/sec and a visualization of the hand.The trials show a high degree of scatter. The residuals show outliers in the experiments. These deviations are mainly due to the individual previous experience of the test subjects in handling VR systems. Nevertheless, significant effects could be identified. A screening experimental design was used in this study. In a follow-up study, detailed investigations with a full factorial experimental design must be performed with the significant factors. The factors will be tested at multiple levels and with a significantly increased number of trials to further increase the accuracy of the mathematical models.

Evaluating Acute Stroke Diagnosis Using Simulation Scenarios.

The methacrylic acid–ethyl acrylate copolymer nanoparticles were prepared using the solvent displacement method. The independent variables were the drug/polymer ratio, surfactant concentration, Polioxyl 40 hydrogenated castor oil, the added water volume, time, and stirring speed, while size, PDI, zeta potential, and encapsulation efficiency were the response variables analyzed. A design of screening experiments was carried out to subsequently perform the optimization of the nanoparticle preparation process. The optimal formulation was characterized through the dependent variables size, PDI, zeta potential, encapsulation efficiency and drug release profiles. In vivo tests were performed in Wistar rats previously induced with diabetes by administration of streptozotocin. Once hyperglycemia was determined in rats, a suspension of nanoparticles loaded with glibenclamide was administered to them while the other group was administered with tablets of glibenclamide. The optimal nanoparticle formulation obtained a size of 18.98 +/− 9.14 nm with a PDI of 0.37085 +/− 0.014 and a zeta potential of −13.7125 +/− 1.82 mV; the encapsulation efficiency was of 44.5%. The in vivo model demonstrated a significant effect (p < 0.05) between the group administered with nanoparticles loaded with glibenclamide and the group administered with tablets compared to the group of untreated individuals.

The open source LARA software suite is designed for guiding manual or automated high-throughput screening experiments. Process planning, data reading, analysis, and visualization are herein explained in a step-by-step guide using exemplary dataset.

Optimization of a solar collector with evacuated tubes using the simulated annealing and computational fluid dynamics

Miniaturized screening and performance prediction of tailored subcutaneous extended-release formulations for preclinical in vivo studies

Modern computer servers require cutting edge technologies to meet their expected high performance. Among several relevant disciplines, power delivery (PD) is a key player in this regard. Efficient and reliable statistical methods to reduce cost while keeping adequate server's performance are highly demanded from the PD perspective. This paper addresses a feasible statistical methodology based on design of experiments (DoE) for evaluating platform's power delivery ingredients. Our methodology explores voltage regulator's intrinsic parameters, compensation networks, non-linear compensation parameters, and the amount of bulk capacitors. Our statistical approach aims at identifying those variables with the largest impact on computer server's PD performance, as well as optimizing them at the system level while achieving cost reduction.

ISHS V International Symposium on Saffron Biology and Technology: Advances in Biology, Technologies, Uses and Market Design of experiments for screening and optimizing factors influencing extraction of bioactive compounds from saffron

Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-sensitive learning for credit card fraud detection. In the first step, useful features are identified using genetic algorithm. Next, the optimal resampling strategy is determined based on the design of experiments (DOE) and response surface methodologies. Finally, the cost sensitive C4.5 algorithm is used as the base learner in the Adaboost algorithm. Using a real-time data set, results show that applying the proposed method significantly reduces the misclassification cost by at least 14% compared with Decision tree, Naive bayes, Bayesian Network, Neural network and Artificial immune system.

For high-speed missiles, aerodynamic heating introduces significant thermal challenges that must be addressed early in the design phase. Accurate temperature prediction is critical, yet traditional methods—like wind tunnel tests or numerical simulations—are often too time-consuming for rapid design cycles. Even fast engineering solvers become bottlenecks during large-scale studies such as Design of Experiments, where thousands of flight conditions must be evaluated. Recent advances in machine learning, enable neural networks to deliver near-instantaneous predictions across large datasets, reducing computation time from hours to seconds. Furthermore, their millisecond-level response times per case make them ideal for real-time or onboard applications, where rapid thermal assessment is essential for guidance, decision-making and protection. In this study, a Physics-Guided Machine Learning (PGML) framework is adopted to predict transient along-surface and through-thickness temperature distributions in high-speed missile radomes. A multi-layer Long Short-Term Memory (LSTM) neural network is trained using data generated from a previously validated physics-based solver benchmarked against CFD and experimental data in the literature. The model incorporates an analytical stagnation temperature relation, which serves as a physics-informed feature to guide the model in both training and testing. This hybridization of data-driven learning with minimal physics injection significantly improves the model’s ability to capture sharp thermal transients and reduces limitations of purely black-box models. The proposed LSTM model delivers fast and reliable predictions, capturing sharp thermal transients with a maximum discrepancy of \(1.71\%\), supporting next-generation thermal design workflows.

This paper presents an engine sizing and cycle selection study of ultra high bypass ratio engines applied to a subsonic commercial aircraft in the N+2 (2025) timeframe. NASA has created the Environmentally Responsible Aviation (ERA) project to serve as a technology transition bridge between fundamental research (TRL 1–4) and potential commercial application (TRL 7). Specifically, ERA is focused on subsonic transport technologies that could reach TRL 6 by 2020 and can be integrated into an advanced vehicle concept to simultaneously meet the ERA project metrics for noise, emissions, and fuel burn. An important variable in exploring the technology trade space is the selection of the optimal engine cycle for use on the advanced aircraft. Previous literature demonstrated the cycle optimization using a design of experiments (DOE) to explore the engine cycle design space for a pre-defined technology package. However, since the optimal engine cycle is dependent upon the specific technology package, this process would have to be repeated to ensure optimal performance for each technology package. With more than 80 technologies to be analyzed, the combinatorial space of technology packages is enormous. As a result, executing a DOE to find the optimum engine cycle for each technology package is infeasible. To address this issue, it is proposed to use surrogate models that encompass the engine cycle and technology design space to enable fast and accurate optimization of the engine cycle for any given technology package. This paper describes the generation and analysis of surrogate models used for technology assessment and cycle optimization of an ultra high bypass geared turbofan engine architecture. The first study in the paper shows that a single surrogate model can be used to accurately simulate both a technology and cycle design space. To demonstrate the proposed surrogate modeling approach, the cycle design space for three different technology packages was analyzed. This study demonstrated that when an optimal cycle is found within the constrained interior of a design space, the surrogate modeling approach is quite accurate. The study also established that the surrogate models can also be used to assess potential cycles at the boundaries or even outside of the region for which they were trained.