Optimum Design Research Articles

A Novel Design Strategy for Cantilever Based MEMS Piezoelectric Vibration Energy Harvesters: FEM Parametric Analysis and Modeling

Vibration-based energy-harvesting (VEH) technology is a viable alternative power source that addresses the issue of battery capacity constraints in portable electronic devices. A novel design strategy for developing a piezoelectric vibrational energy harvester (PVEH) based on a microelectromechanical system (MEMS) is proposed in this paper. Through FEM simulation and analysis, a correlation between the proof mass coverage (PMC) and piezoelectric coverage (PEC) for energy harvesters with different beam geometries is developed. The results show that the output power and resonant frequencies of the energy harvester are strongly dependent on the amount of PMC, PEC, and beam geometry. The optimum piezoelectric layer length for a PVEH depends on the amount of PMC and geometry of the beam. Moreover, as the PMC increases, the output power is found to be increasing. The structure with 80% PMC provides maximum output power. Also, a trapezoidal beam will give a lower resonant frequency than a rectangular beam. Thus the optimum design methodology for a cantilever-based MEMS PVEH in a given layout is to select the trapezoidal-shaped beam with a PMC of 60% to 80% and piezoelectric layer length equal to the beam length. This method would provide optimised resonant frequency and output power performance. Additionally, an analytical model is developed for the optimised cantilever PVEH based on trapezoidal geometry, and the simulation findings are validated with the analytical results.

Optimum design of flattening process for roll-formed door impact beam with GPa-grade steel

Optimum design of flattening process for roll-formed door impact beam with GPa-grade steel

Design of Experiments and Optimization of Monacolin K Green Extraction from Red Yeast Rice by Ultra-High-Performance Liquid Chromatography.

Monacolin K (MK), in red yeast rice (RYR) in the forms of lactone (LMK) and hydroxy acid (AMK), is known for its anti-hypercholesterolemic activity. Under the rising demand for natural bioactive molecules, we present a green ultrasound-assisted extraction (UAE) optimization study for MK in RYR. The development and validation of a fast, sensitive, selective, reproducible, and accurate ultra-high-performance liquid chromatography (UHPLC) method coupled to diode array detection for LMK and AMK allowed us to evaluate the MK recovery in different extract media. Firstly, the ethanol comparability to acetonitrile was assessed (recovery of 80.7 ± 0.1% for ethanol and 85.5 ± 0.2% for acetonitrile). Then, water/ethanol mixtures, with decreasing percentages of organic solvent, were tested by modulating temperature and extraction times. Water extractions at 80 °C for 10 min produced MK yield > 85%. Thus, UAE conditions were optimized by a DOE study using a water-based formulation (mouthwash). The optimal total MK extraction yield (86.6 ± 0.4%) was found under the following conditions: 80 °C, 45 min, 5 mg mL-1 (RYR powder/solvent). Therefore, the new single-process green approach allowed the simultaneous direct extraction of MK and mouthwash enrichment (MK concentration = 130.0 ± 0.6 µg mL-1), which might be tested for the prevention and treatment of periodontitis or oral candidiasis.

Application of quality by design in optimization of nanoformulations: Principle, perspectives and practices.

Nanoparticulate drug delivery systems (NDDS) based nanoformulations have emerged as promising drug delivery systems. Various NDDS-based formulations have been reported such as polymeric nanoparticles (NPs), nanoliposomes, solid lipid NPs, nanocapsules, liposomes, self-nano emulsifying drug delivery systems, pro liposomes, nanospheres, microemulsion, nanoemulsion, gold NPs, silver NPs and nanostructured lipid carrier. They have shown numerous advantages such as enhanced bioavailability, aqueous solubility, permeability, controlled release profile, and blood-brain barrier (BBB) permeability. This advantage of NDDS can help to deliver pure drugs to the target site. However, the formulation of nanoparticles is a complex process that requires optimization to ensure product quality and efficacy. Quality by Design (QbD) is a systemic approach that has been implemented in the pharmaceutical industry to improve the quality and reliability of drug products. QbD involves the optimization of different parameters like zeta potential (ZP), particle size (PS), entrapment efficiency (EE), polydispersity index (PDI), and drug release using statistical experimental design. The present article discussed the detailed role of QbD in optimizing nanoformulations and their advantages, advancement, and applications from the industrial perspective. Various case studies of QbD in the optimization of nanoformulations are also discussed.

Reliability-based Design Optimization (RBDO) Study Applied to the Thermal Management of a Multi-Led Chip Package

Solid-state lighting based on LEDs is used in various applications, including display, communications, etc. However, the high junction temperature is still challenging due to the LED chip’s reduced light output and lifetime. To face this challenge, a thermal study was done to determine junction temperature TJ of a multi-led chip package. Then, a sensitivity analysis of different materials was performed using the Sobol method to identify the parameters that most influence the junction temperature. To calculate the probability of failure of the model, the FORM-SORM and Monte Carlo methodologies were employed in this study. It was obtained that the probability of failure of the LED package is roughly 20%. An optimum design was created using reliability-based design optimization (RBDO) to lower this percentage. After application of this method, the junction temperature was lowered by 11% and the reliability level increased from 80% to 91%.

Searching for optimum design and burnable absorber for controlling the reactivity of U.S. Supper critical water reactor (SCWR)

Numerous research endeavours are currently underway to advance supercritical water reactors (SCWR), acknowledged as a cornerstone among Generation IV nuclear reactor designs. Evaluation and managing the reactivity of the reactor is a vital issue in the reactor operation. This research seeks to identify efficacious burnable absorber (BA) materials and determine an optimal spatial distribution within the fuel assembly to regulate reactivity levels effectively. Gadolinium, erbium and Lutetium have been suggested as BA in the form of integral burnable absorber (IBA) rods ((UO2 + Gd2O3), (UO2 + Er2O3) and (UO2 + Lu2O3)). Two SCWR assembly models, each featuring varied quantities and distributions of BA, have been examined. Various concentrations of the suggested BAs have been examined in the suggested models to verify the optimum cases. Burnup analyses have been conducted to evaluate the proposed cases. Different alloys of BAs including B4C+Dy2O3 and B4C+Sm2O3 have been investigated in the control rod and compared with the standard alloy B4C.

Monte Carlo analysis of a 400N electric pump-fed engine powered by green propellants: Key trends towards an optimal subsystem design

This paper presents a Monte Carlo analysis for electric pump-fed systems. By leveraging the tool, it is possible to determine the optimal values of the main parameters involved in the design, operation, and selection of the pressurization system, enabling an agile system design. The study is conducted for a 400 N electric pump-fed engine powered by green propellants. This process aims at identifying the optimal operational conditions towards wet mass minimization and, eventually, assess whether a system featuring green propellants and electric pump feeding can represent an environmentally friendly alternative to conventional pressure-fed systems powered by MMH and NTO, known for their high toxicity and operational costs. The results obtained confirm similar performance to the state of the art, both in wet mass and delta-v capabilities, these last ones analyzed for different mission profiles involving orbit raising and RAAN phasing. Finally, based on the results obtained, the paper highlights the key trends towards an optimum design of electric pump-fed systems.

Optimum design of uniform and non-uniform infill-coated structures with discrete variables

This article introduces a novel computer-aided procedure to design optimised coated structures with precise shell thickness control using the Smallest Univalue Segment Assimilating Nucleus operator and a novel augmentation-projection technique. Structures with heterogeneous sections, or coated structures, combine two different materials for the nucleus and the shell, which are generally chosen so that the material in the infill is lighter and the material in the coating is stiffer, which in this work are supposed homogeneous. Solving the interface problem requires material properties interpolation equations that consider three material phases, accurate placement of the coating over the base material, and precise control over the coating's thickness. The formation of the coating is controlled by the Smallest Univalue Segment Assimilating Nucleus, an edge detection operator developed in Digital Image Processing. The coating's thickness is controlled by an innovative methodology consisting of the projection of an augmented contour field, which is shown to create a constant thickness coating around the material domain. The optimisation problem is solved with the Sequential Element Rejection and Admission method. The validity of the procedure has been verified by solving various numerical application examples.

A computationally efficient approach of tuned mass damper design for a nuclear cabinet based on two-step machine learning and optimization methods

Enhancing nuclear power plant (NPP) safety is demanded because of the recent beyond-design-basis earthquake near a NPP. Therefore, research on improving the seismic performance of the electrical cabinet, which ensures the safe operation of NPPs, is needed. In this paper, a tuned mass damper (TMD) is employed to control the seismic response of cabinet. To design the TMD, we employ existing design equations or perform numerical model–based optimization. However, limitations, such as inconsistencies with targeted control of the load and structure, the possibility of converging a local solution, and the high cost of numerical analysis. Therefore, this paper proposes a two-step machine learning and optimization method. Such an approach is utilized to find the optimal global design solution and reduce numerical analysis costs. Each step involves the design of experiment (DOE), response surface, and optimization. Notably, range setting in the DOE accounts for the difference between each step. In the first step, the sampling range is widened to determine the relationship between the design variables and the cabinet's response, and in the second step, the sampling range is narrowed depending on the result of the first step. Consequently, the proposed method reduced the cabinet's response by 35.4 % on average and numerical analysis cost declined by 1/3.

Design and testing of an intelligent variable-rate spraying system based on LiDAR-guided application

Traditional plant protection machinery cannot apply pesticides according to the actual growth demand of crops, which causes a lot of pesticide waste and serious environmental pollution. Based on this, an intelligent variable-rate application system based on 3D LiDAR (light detection and ranging) scanning and PWM (pulse-width modulation) control for maize canopy was developed in this paper. After completing the performance testing, the system was mounted on a lightweight high-clearance self-propelled sprayer by remote controlling with an operating width of 6.0 m, and a ground gap of 2.0 m. And the actual operational performance of the sprayer showed that the prediction error of LiDAR for plant height was within 10%, which illustrated the accuracy of the detection system and the reliability of the variable application based on the plant height change. And the variable-rate system could adjust the spray volume in real-time according to the plant height change. The penetration rate of the upper, middle, and bottom layers maintained a relatively stable state (upper > middle > bottom) at variable-rate application and could reach the normal application level. This study can provide new ideas for efficient pests and diseases control of high stalk crops, especially suitable for small fields, and gives a reference for the structural design of new precise variable-rate application machinery and performance optimization of efficient application technology.

Optimized vascular network by stereolithography for tissue engineered skin.

This paper demonstrates the essential and efficient methods to design, and fabricate optimal vascular network for tissue engineering structures based on their physiological conditions. Comprehensive physiological requirements in both micro and macro scales were considered in developing the optimisation design for complex vascular vessels. The optimised design was then manufactured by stereolithography process using materials that are biocompatible, elastic and surface bio-coatable. The materials are self-developed photocurable resin consist of BPA-ethoxylated-diacrylate, lauryl acrylate and isobornylacrylate with Irgacure® 184, the photoinitiator. The optimised vascular vessel offers many advantages: 1) it provides the maximum nutrient supply; 2) it minimises the recirculation areas and 3) it allows the wall shear stress on the vessel in a healthy range. The stereolithography manufactured vascular vessels were then embedded in the hydrogel seeded with cells. The results of in vitro studies show that the optimised vascular network has the lowest cell death rate compared with a pure hydrogel scaffold and a hydrogel scaffold embedded within a single tube in day seven. Consequently, these design and manufacture routes were shown to be viable for exploring and developing a high range complex and specialised artificial vascular networks.

Using tire derived aggregates (TDA) as infill material for wave barriers

Ground-borne vibrations caused by construction activities and highway and railway traffic may disturb adjacent structures and sensitive machines. Thus, studies on isolating this type of vibrations have accelerated in recent years. Few experimental work investigated the use of rubber-soil mixtures as infill material for vibration mitigation. However, despite its high damping capacity, almost no experimental work has been conducted to evaluate the use of Tire Derived Aggregates (TDA) as infill material for trench barriers. This paper aims to investigate the use of TDA trenches as a wave barrier. Also, the performance of buried TDA trenches was considered in this work. A series of vibration tests were conducted in a steel tank with dimensions of 2.75 m long, 2.25 m wide, and 1.80 m deep. The trench depth and frequency varied from 0.2 to 0.6 m and 100–200 Hz, respectively. The results showed that TDA trenches had a superior performance compared to open trenches. TDA trenches could reduce the vertical vibration amplitude by up to 30 % more than open trenches. Also, open and TDA trenches performed better in mitigating the vertical vibrations compared to the other directions. Furthermore, for optimum design of open and TDA trenches, the normalized trench depth should be between 1 and 1.3 D. Covering TDA trenches with soil was found to decrease their vibration isolation significantly at normalized burial depth larger than 0.15.

Dynamic reflectivity of an opto-acoustic three-layer transducer for picosecond ultrasonic measurements

We present an analysis of a three-layered structure used for both launching and detecting picosecond acoustic waves. To enhance the optical sensitivity to the acoustic disturbances, a cavity is designed, which acts as a Fabry–Perot interferometer. We use analytic modeling based on dual optical and acoustic transfer matrix formalism to analyze the coupled optical and acoustic wave propagation. Assuming a three-layer transducer made of an optical cavity sandwiched between two thin metallic layers, the model allows for mastering of the coupled optical and acoustic responses, which leads to an optimum design of the structure, and it highlights the various acoustic contributions to the reflectivity changes. The sensitivity of this three-layered structure to acoustic disturbances is compared to the numerical predictions we performed for the standard opto-acoustic transducer made of a single metallic layer.

The design of ‘opportunistic’ or sparse onshore seismic surveys

Seismic surveying is the preferred subsurface exploration technique, but data acquisition can be problematic in populated areas and especially in urban areas. In this paper, I describe a method to design “opportunistic” or sparse surveys that utilize existing roads and tracks and whose objective is to allow the acquisition of a data set especially suited to the application of 5D regularization. The method incorporates six stages: theoretical design, identification of possible line locations, geometry creation, geometry evaluation, survey optimization, and the creation of infill points. Unlike conventional designs, this process requires the calculation of specific metrics that indicate the suitability of the design for regularization. The process also requires multiple iterations of the evaluation/optimization/infill stages, some of which can be automated, to generate the optimum design. Given the bespoke nature of the method, a specialized MATLAB package was developed to implement the process, which also incorporates the numerous high-performance algorithms required.

Optimization Design of a 6 T Fast Cycling Dipole Prototype With Round Cable

Optimization Design of a 6 T Fast Cycling Dipole Prototype With Round Cable

Optimization Design of PMSM Sensorless Control Using Generalized Integrator

Optimization Design of PMSM Sensorless Control Using Generalized Integrator

Optimization Design of 30 MJ/5 MW LIQHYSMES Toroidal D-Shaped Magnet

Optimization Design of 30 MJ/5 MW LIQHYSMES Toroidal D-Shaped Magnet

Multi-drug extraction using octanol supported rotating cellulose paper disc (RPD) device from complex biological matrices: Fabrication and application in forensic case work

For the first time, a novel rotating paper disk (RPD) device to simplify the sample preparation process for the analysis of a broad spectrum of analytes with varying log Kow (i.e., 0.65–5.4) values is introduced.These analytes encompass diverse classes, including benzodiazepines (such as diazepam, chlordiazepoxide, flurazepam), barbiturates (such as barbital, allobarbital, phenobarbital), antihistamines (i.e., pheniramine, chlorpheniramine), antidepressants (such as fluoxetine, amitriptyline, venlafaxine), antipsychotics (like clozapine, chlorpromazine), opioids (i.e., tramadol) and z-drugs (such as zolpidem). RPD synergistically utilizes the adsorption capabilities of cellulose paper (solid substrate) in conjunction with 1-octanol supported onto the paper. In order to fabricate the RPD device, a magnetic bar was sandwiched between octanol supported cellulose paper layers. RPD was then directly immersed into the diluted biological samples (postmortem biological matrices i.e blood and urine) and allowed to rotate at 400 rpm for 30 min for optimum adsorption of target analytes. Subsequently, adsorbed analytes were back-extracted into 1 mL of methanol without any external diffusion process and finally determined by gas chromatography-mass spectrometry (GC-MS). Design of experiment-based optimization was performed using Box-Behnken design (BBD) for the optimization of significant factors of sample preparation such as pH of the sample, stirring speed, extraction time and ionic strength. Under the optimized conditions, the method was found to be linear in the range of 0.1–10 μg mL−1 for blood and urine samples. Limit of quantification (LOQ) for blood and urine samples were determined within the range of 0.084–0.099 and 0.060–0.078 μg mL−1, respectively. Intra-day and inter-day precisions were less than 6 and 10%, respectively. Good relative recoveries in the range of 50.4–98.2%, were obtained for the proposed procedure. Furthermore, the proposed method has been effectively applied in forensic toxicology case work. Additionally, the proposed method underwent evaluation for its whiteness and blueness using the RGB12 algorithm and BAGI software, respectively.

Box-Behnken design in optimization of the green liquid chromatographic method for the quantification of Afatinib in drug product: AQbD approach

The aim of this research work was to quantitatively determine the tyrosine kinase inhibitor, Afatinib, in tablets using an eco-friendly chromatographic method. The development of a green liquid chromatographic method using analytical quality-by-design (AQbD) approach principles to estimate Afatinib via Box-Behnken design. A simple and effective green liquid chromatographic method was developed and validated. The analysis was done on the Zodiac C18 column by isocratic elution mode. Mobile phase composed of pH 3.0 potassium dihydrogen phosphate buffer and ethanol in the ratio of 65:35%V/V. The flow rate was set to 0.6 mL/min. with an injection volume of 10 µL and at detection wavelength of 254 nm. The optimized method was found to be specific, linear, precise, accurate, and robust. Established linearity in the range of 24.6 to 73.9 µg/mL. The developed method exhibited excellent greenness. Based on the greenness assessment tools, the analytical eco-score value of 94 and the pictograms of NEMI, Modified NEMI, GAPI, Complex GAPI, AGREE, and AGREE prep expressed the method’s greenness. Hence, the AQbD approach based green method and the validation data indicate that the method can be employed in routine analysis for the determination of Afatinib in pharmaceutical dosage form in quality control laboratory use.

A novel logistic regression-embedded 0–1 mixed-integer probabilistic robust design model to obtain optimum sustainable factor settings

Quality by design (QbD) focuses on satisfying the needs of products or processes when aiming for variance reduction. On the other side, sustainability is associated with carrying on to conduct so over time. The research gap is also addressed in sustainability and QbD in this paper. Next, robust design (RD) is a suitable approach to QbD to reduce the process or product variation regarding quantitative and qualitative design factors. For a number of situations, a binomial random response variable is considered to decrease the variability of the production process. Thus, this paper is four-fold. First of all, a modified distance-based criterion is developed to generate the design matrix when dealing with quantitative and qualitative design factors. Second, a logistic regression technique is presented for modeling responses when the random response variable is binomial. Third, a new logistic regression-based 0–1 mixed-integer probabilistic RD model is proposed to find optimum sustainable design factors. Fourth, an Industry 4.0 associated case study is performed in order to design sustainable process development, and the comparison and verification studies are also presented to show the effectiveness of the proposed probabilistic model. Further, managerial insights are provided based on the sustainable development goals (SDGs).