Subsea separation is an attractive and economic solution to develop deep offshore oil and gas fields producing fluid without hydrate or wax. The subsea separation system should be reliable to ensure successful operation in a wide range of multiphase flow regime. A subsea separator can avoid or simplifying costly surface platforms of floating vessels, as well as being an efficient tool to enhance hydrocarbon production. One solution of interest is the separation and re-injection of water at the seabed to avoid bringing the water up to the surface facility. In this study, liquid-liquid flow characteristics inside in-line type subsea separation system are investigated for the design of subsea separation system. The separation efficiency of the subsea separator is determined through experiments that are the liquid-liquid phased separation. Also internal swirl element (ISE) modeling of the separator was optimized. The analysis results were utilized to improve the reliability and efficiency of the subsea separation system.

Electrowetting heat pipes (EHPs) are a newly conceptualized class of heat pipes, wherein the adiabatic wick section is replaced by electrowetting-based pumping of the condensate (as droplets) to the evaporator. Specific advantages include the ability to transport high heat loads over long distances, low thermal resistance and power consumption, and the absence of moving mechanical parts. In this work, we describe characterization of key microfluidic operations (droplet motion and splitting) underlying the EHP on the International Space Station (ISS). A rapid manufacturing method was used to fabricate the electrowetting device on a printed circuit board. Key device-related considerations were to ensure reliability and package the experimental hardware within a confined space. Onboard the ISS, experiments were conducted to study electrowetting-based droplet motion and droplet splitting, by imaging droplet manipulation operations via pre-programmed electrical actuation sequences. An applied electric field of 36 Volts/um resulted in droplet speeds approaching 10 mm/s. Droplet splitting dynamics was observed and the time required to split droplets was quantified. Droplet motion data was analyzed to estimate the contact line friction coefficient. Overall, this demonstration is the first-ever electrowetting experiment in space. The obtained results are useful for future design of the EHP and other electrowetting-based systems for microgravity applications. The testing was performed under the Advanced Passive Thermal eXperiment (APTx) project, a project to test a suite of passive thermal control devices funded by the ISS Technology Demonstration Office at NASA JSC.

In reliability-based mechanical design, reliability replaces the traditional factor of safety as the measurement index of the safety of mechanical components. More than 90% of metal components under cyclic fatigue loadings in industries fail because of fatigue. The P-S-N curve fatigue theory (Probability - Stress level - Number of cycles) is one of the current important fatigue theories. It is very important to know how to determine the reliability of components under different loading-induced cyclic stresses for reliability-based mechanical design. The Monte Carlo method is a powerful numerical simulation in almost every field such as optimization, numerical integration, and generating draws from a probability distribution. Literature reviews show the Monte Carlo method is successfully implemented to estimate the reliability of components under single loading-induced cyclic stress. However, there is little literature about implementing the Monte Carlo method to estimate the reliability of components under multiple loading-induced cyclic stress by using the P-S-N curve fatigue theory. The purpose of this paper is to develop a new Monte Carlo computational algorithm to calculate the reliability of components under several cyclic loadings using the P-S-N curve fatigue theory. Two key concepts in the widely-accepted Miner rule in fatigue theory are that fatigue damage is linear cumulative and the fatigue damage because of different cyclic stress is independent. Based on these two key concepts, this paper has successfully developed a new Monte Carlo computational algorithm to calculate the reliability of components under multiple loading-induced cyclic stresses using the P-S-N curve fatigue theory. The results obtained by the developed computational algorithm is validated by results obtained from two published methods. The results by the developed computational algorithm is again validated by the K-D probabilistic model. Based on validation studies, the relative differences in the results between the proposed method and the published methods are in the range of 0.66% to 2.98%. Therefore, the developed Monte Carlo computational algorithm is validated and can provide an acceptable estimation of the reliability of components under several cyclic fatigue loadings using the P-S-N curve fatigue theory.

Microgrids are an essential element of smart grids and are identified as a key component for improving power reliability and quality, increasing system energy efficiency, and providing the possibility of grid-independence to individual end-user sites. Microgrids support a flexible and efficient electric grid by enabling the integration of growing deployments of distributed energy resources such as solar and wind power. According to a recent Navigant report, spending on microgrid enabling technologies will reach 112 billion dollars by 2026. While the development of microgrids and distributed energy technologies are underway, a recent Department of Energy jobs report indicates that the U.S. does not have enough skilled workers to fill 1.5 million new energy jobs by 2030. Furthermore, even the most experienced workers often lack the updated skills and training needed to successfully support the advanced systems, tools and devices that make up microgrids. Hence it is important to develop new courses that can equip our students with the necessary technical skills to enter the microgrids job market. This paper presents the development of a new course on microgrids and distributed energy resources. This course is a follow up to a previously developed course on Smart Grids. The paper will discuss the course content, the pedagogical approach used to deliver the course, and some of the students’ projects and presentations.

Air bearing is future main supporting way of high-speed machinery such as turbocharger, micro gas-turbine engine. Foil bearing is a new type of air bearing which is lubricated by the thin-film air with its self-adapting elastic foil structure. It has many significant advantages such as non-pollution, longer working life, higher reliability, and lower friction loss. Different from foil journal bearing, in present the study of foil thrust bearing is extremely insufficient, especially about how to accurately predict the pressure distribution and efficiently improve the bearing capacity. The pressure distribution prediction of foil thrust bearing air film directly impacts the bearing stiffness and damping design, and then influences bearing capacity. The Reynolds equation commonly used to do such estimation is not accurate enough since the influence of temperature on air property parameters is ignored. The inaccurate prediction leads a catastrophic reduction to the bearing performance. In order to solve this problem, we propose a model to accurately predict the pressure distribution and capacity of foil thrust bearing using CFD method, as well estimating the relationship between air film clearance thickness, rotation speed, environment temperature and the capacity. Firstly, we simulate the pressure distribution of air film and then evaluate the simulation result by constrained experiments. We also correct the simulation by using modified air parameters obtained from experiment. The experimental results indicate our corrected simulation model is accurate with error less than 4%. Secondly, we compare simulation and experiment pressure results under different conditions. The model accuracy sensitivity varies within 10% under different rotation speed, air film clearance thickness and environment temperature. Finally, we use corrected model to analyze capacity impact parameters. We find the capacity of bearing increases with the decreasing of average air film clearance thickness under fixed speed of the thrust disc. The smaller clearance thickness is, the more influence its variation has on the bearing capacity. Meanwhile, the capacity of the bearing decreases with the reducing thrust disc speed under constant clearance thickness, and it decreases more obviously in the lower speed. The capacity reaches its largest under 200 °C and it falls with the increases or decreases of environment temperature. The model in this paper provides important theoretical foundation when designing the stiffness, damping and temperature control of each bearing area.

Non-contacting annular seals are used in rotating machinery to reduce the flow of working fluid across a pressure differential. Helical and labyrinth grooved seals are two types of non-contacting annular seals frequently used between the impeller stages in a pump and at the balance drum. Labyrinth seals have circumferential grooves cut into the surface of the rotor, the stator, or both. They function to reduce leakage by dissipating kinetic energy as fluid expands in the grooves and then is forced to contract in the jet stream region. Helical groove seals have continuously cut grooves on either or both the rotor and stator surfaces. Like labyrinth seals, they reduce leakage through dissipation of kinetic energy, but have the added mechanism of functioning as a pump to push the fluid back towards the high-pressure region. Previous work has shown that mixed helical-labyrinth seals with labyrinth grooves on stator and helical grooves on rotor or labyrinth grooves on rotor and helical grooves on stator have an approximately 45% lower leakage than an optimized helical groove seal with grooves just on the stator in a high pressure application. The primary objective of this study is to determine whether the same performance gains can also be achieved in a low pressure application. Simulations were run in ANSYS CFX for seal designs with a helical stator and labyrinth rotor. Several labyrinth design parameters including the number of grooves and the groove width and depth are varied while the helical variables such as the groove width and depth as well as helix angle are kept constant. The data obtained are analyzed using backward regression methods and various response plots to determine the relationship between the design parameters and mass flow and power loss. The optimized helical design was simulated and the axial pressure profiles of the designs were compared to analyze the mechanism of the mixed helical-labyrinth seal. Then, the same labyrinth seal designs were simulated for a labyrinth rotor and a smooth stator to determine whether the optimal number of grooves, groove width and groove depth change due to the helical stator. The findings of this study show the effectiveness of mixed helical labyrinth grooved seals for both low and high pressure cases, and thus their efficiency and reliability for numerous industrial applications.

In oil and gas industry, machineries and mechanical components are designed with high reliability to meet the demand of the oil field. Rotating machinery is a widely used equipment and any failure of critical components within the machinery could lead to delays and large expenses. Failure of rotary seal is one of the foremost causes of breakdown in rotary machinery and such a failure can affect the other process operations in oil and gas plants. Assessing seal degradation and severity estimation are very important for maintenance decision-making. Extracting meaningful and sensitive features that can show seal degradation from raw signals is a challenging task of degradation assessment. However, no extensive works are dedicated in this area of seals. In this paper, we perform accelerated aging and testing to capture the behavior of seals through their cycle of operation and demonstrated a statistical time domain feature based approach for extracting the sensitive features that can show seal degradation. Out of eleven statistical features extracted, seven extracted features such as mean, RMS, maximum, squared mean rooted absolute amplitude, impulse factor, crest factor, margin factor are found to be significant factors which have a potential to differentiate severity levels in seals. The findings from our work show that our approach has a potential to assess the severity in seals. As a possible extension, extracted features can be used to build a classification model to classify severity in seals which could be of great interest to the users and manufacturers of rotary seals.

Spring operated pressure relief valves (SOPRVs) are essential components of technical systems. As parts of safety systems, they protect people and the environment from technological hazards. Their ability to open at a predefined pressure is considered the most important feature. The reliability of this function depends on numerous operational and design factors. In this paper, we examine the effects of design measures on the mechanical loads in seat seals of SOPRVs. In particular, we evaluate the applicability of the principle of non-uniform system stiffness in order to systematically control the mechanical loads in seat seals for an exemplary case of a flat faced soft seated SOPRV. We systematically vary design parameters and accurately estimate the contact stresses as well as the set pressure by performing non-linear finite element analyses. We focus on the quasi-static case of a closed seal, since dynamic effects of the opening and closing processes are not within the scope of this work. In our contribution, we show that the application of these design measures can significantly influence both the initial contact stresses and the set pressure at a constant spring force. In particular, the effects of the taper angle are analyzed and discussed.

This paper proposes reliability-based optimal design of a micro-grid system under service disruptions due to natural disasters. The objective is to determine the minimum number of generators and their distributions in the micro-grid so that the system’s recoverability (or resilience) and operation efficiency can be guaranteed under random failure scenarios of the power transmission lines. Power flow analysis combing with the Monte Carlo simulation (MCS) are used for uncertainty propagation analysis to quantify the system’s recoverability distribution and the transmission efficiency distribution under random failure scenarios of the transmission lines. The optimal allocation of the generators is much more reliable compared to the deterministic solutions without considering various uncertainties in the system. The proposed work is demonstrated through a 12-bus power system.

Rolling element bearings are very important and highly utilized in many industries. Their catastrophic failure due to fluctuating working conditions leads to unscheduled breakdown and increases accidental economical losses. Thus these issues have triggered a need for reliable and automatic prognostics methodology which will prevent a potentially expensive maintenance program. Accordingly, Remaining Useful Life (RUL) prediction based on artificial intelligence is an attractive methodology for several researchers. In this study, data-driven condition monitoring approach is implemented for predicting RUL of bearing under a certain load and speed. The approach demonstrates the use of ensemble regression techniques like Random Forest and Gradient Boosting for prediction of RUL with time-domain features which are extracted from given vibration signals. The extracted features are ranked using Decision Tree (DT) based ranking technique and training and testing feature vectors are produced and fed as an input to ensemble technique. Hyper-parameters are tuned for these models by using exhaustive parameter search and performance of these models is further verified by plotting respective learning curves. For the present work FEMTO bearing data-set provided by IEEE PHM Data Challenge 2012 is used. Weibull Hazard Rate Function for each bearing from learning data set is used to find target values i.e. projected RUL of the bearings. Results of proposed models are compared with well-established data-driven approaches from literature and are found to be better than all the models applied on this data-set, thereby demonstrating the reliability of the proposed model.

Diamond sawblade is an efficient tool to building renovation or demolition. Concrete used in construction is a typical composite material with random distribution, which is difficult to accurately identify and predict even under the same processing conditions, and tool life of diamond sawblade is difficult to control. In this paper, by cutting out single component of the hard and soft aggregate separately from concrete, the single component and concrete experiments were carried out to understand the sawing characteristics of different components. The wavelet decomposition was used to analyze the characteristic of each frequency band of the different components sawing force and vibration signals, and the sensitive frequency bands after correlation coefficient and energy ratio variation of each wavelet layer were extracted to judge the bluntness status of sawblade. By taking the Root-Mean-Square (RMS) value, the energy ratio of d2 and d5 wavelet layers and the standard deviation of sawing force and vibration signal as the characteristic values of the sawblade, a neural network optimized by bat algorithm was established to analyze the concrete processing signals and predict the working state of the sawblade. Evidence theory was adopted to combine the prediction results of sawing force and vibration samples to increase the overall prediction accuracy and reliability. The test sample showed that this method can correct inconsistent individual sensor predictions while being as close to the actual status value as possible. It provides an effective tool life prediction way of the diamond sawblade and a theoretical method for the monitoring of non-metallic materials with inhomogeneous components.

This paper establishes the baseline for incorporating the Internet of Things (IoT) into the Reliability-Risk model. The authors developed the original Reliability-Risk model as a “trade-off” tool for ranking conceptual designs as a function of reliability. We summarize the original Reliability-Risk model and algorithm and discuss the process of updating the standard Integration Definition Function Modeling (IDEF0) technique with the IoT. Inserting the updated IDEF0 into the Reliability-Risk modeling framework creates a dynamic closed-loop system. We identified a concept for using a probabilistic workflow to automate the new closed-loop system and discuss a Reliability-Risk sensitivity approach. The Reliability-Risk model ranked five conceptual packaging designs against 17 criteria for incorporation into the supply chain. The authors use a Multi-Criteria-Decision System (MCDS) to establish the rankings. The paper re-visits the original example to include data (the IoT) such as shock, temperature, and humidity obtained from various nodes in the logistics cycle. After the sensor data are incorporated, updated systems specification and reliability models resulted in a new ranking. We will discuss the results of the rankings. Current research in developing the Digital Twin and Digital Thread are lacking in the area of logistics modeling. The incorporation of Discrete Event Simulation models to simulate transportation, handling, and storage shows promise to address these shortcomings. Therefore, we will briefly discuss our approach on incorporating Discrete Event Simulation modeling into the Reliability-Risk-IoT model to create a “logistics twin.”

Boiler systems have many factors that affect the efficiency of the air conditioning process. A traditional control system is complicated and requires physical hardware and software programming. The modern operator console is more compact and works better. Regardless of the boiler system, the operator can run it automatically, allowing changes in many variables such as fuel input, air combustion, water feed flow, steam temperature, etc. In fact, it is impossible to control all of these variables simultaneously in the manual mode. Modern systems have been developed to a high degree of precision that can provide excellent results in maintaining high levels of efficiency, reliability, and safety. The Programmable Logic Controller (PLC) was developed in 1968, and since then it has improved rapidly and applied to many manufacturing industries. Besides the Allen Bradley PLC, other modules were developed such as Human Machine Interface (HMI), Variable Frequency Drives (VFDs), and safety components. In this paper, the Allen Bradley PLC ControlLogix family 1756 model is used to control the boiler system. In addition, this work focuses on improving the boiler efficiency by comparing between the manual and automatic control the fuel-air ratio.

The Stirling engine is a high efficiency and high reliability energy converter which is expected to be a solution for future space power generation and commercial applications. One of the key components in a Stirling engine, to keep high efficiency, is the regenerator. Currently, woven screens or random fibers are mostly used as the regenerator material. However, since both woven screen and random fiber regenerators are composed of wires, the flow across the wires is similar to cylinders in cross flows. As a result, flow separation occurs and the regenerator results in high friction losses and thermal dispersion. In the previous study, a robust foil type regenerator is designed and CFD analysis of the regenerator was conducted to predict the friction coefficient and the thermal efficiency under oscillating flow conditions. In this research, a regenerator test bench was designed and constructed to investigate the friction coefficient and thermal efficiency of the regenerator. The experiment conditions are decided based on the one-dimension thermodynamic modeling software SAGE. The experiment result shows that the friction coefficient of the experiment is close to CFD prediction at high Reynolds number.

Laparoscopic Devices are known to be used in Minimal Invasive Surgeries. However, the devices are unable to transmit the spectrum of feedback of the tissue to the hands of the surgeon, which makes the surgical procedure difficult. We have demonstrated a parameterization for the loss in feedback due to backlash and friction. Backlash is correlated to the joint clearances while friction correlates to joint clearance as well as the surface finish of mating pairs, though joint clearances don’t affect the friction coefficient largely. The laparoscope behavior has been dynamically modeled to understand and predict the behavior. Also, the cost to manufacture the graspers has been weighed against force bandwidth and reliability of improving the joint tolerances of the device so that it is able to transmit the desired force bandwidth. We conclude that to achieve kHz force bandwidth through purely mechanical means would entail prohibitively expensive manufacturing means and hence, we propose an alternate design. The alternate design makes the system deterministic without uncertainty in the position of the joint pins. The kinematically constrained joints in the new device completely transmit the input force spectrum at frequencies of multiple kHz. The non-sophisticated alteration in the original device doesn’t significantly alleviate the cost and eludes the loss of tactile sensation.

The blowpipe is used to transmit hot blast air in blast furnace operation, which is continually manipulated and part of a high-temperature and high-pressure environment. In order to optimize blowpipe functionality and reliability, computational fluid dynamics (CFD) was applied to simulate the flow pattern of hot blast air, to generate details of input conditions for mechanical simulation of thermal stress, and to help identify areas of failure corresponding to high stresses and plastic strain. In this investigation, parametric studies were conducted to study the effects of thermal paper thickness, thermal paper length, and refractory thickness on the thermal and stress distributions. The results of this investigation can offer useful information for new blowpipe designs with reduced failure and better reliability.

For the development of design rules for nuclear piping using the Load and Resistance Factor Design (LRFD) method, the probabilistic properties of steel, namely, the mean value, bias, coefficient of variation, and probability distribution are needed. The paper presents background information for the existing material tables in the ASME Boiler and Pressure Vessel Code, Section II. Then it investigates the probabilistic properties for the most representative materials used for nuclear piping such as a carbon, stainless austenitic, and low alloy steels. Properties up to temperature 700°F are examined through a review of studies for the mechanical behavior of these materials. The paper discusses approaches for grouping materials in broader categories than the consideration of each type of steel separately. The impact of the steel probabilistic properties on the development of LRFD equations and the associated target reliability index is provided.

With the advent of 3D printing technology growing fast, it is important to determine the properties of objects made by this new process. Since some sensitive products, used in life-sustaining applications, are currently made by 3D printing, the reliability and durability of such components must be closely examined. Mechanical properties of metals may vary with the direction of heat transfer during solidification. This study presents the results of tensile tests, conducted at microscale on samples extracted from 3D printed metals. The implications of the variation of strength with solidification direction on the reliability of such metallic objects are discussed.

The characterization of single cells within heterogeneous populations has great impact on both biomedical sciences and cancer research. By investigating cellular compositions on a broad scale, pertinent outliers may be lost in the sample set. Alternatively, an investigation focused on the behavior of specific cells, such as circulating tumor cells (CTCs), will reveal genetic biomarkers or phenotypic characteristics associated with cancer and metastasis. On average, CTC concentration in peripheral blood is extremely low, as few as one to two per billion of healthy blood cells. Consequently, the critical element lacking in many methods of CTC detection is accurate cell capture efficiency at low concentrations. To simulate CTC isolation, researchers usually spike small amounts of tumor cells to healthy blood for separation. However, spiking tumor cells at extremely low concentrations is challenging in a standard laboratory setting. We report our study on an innovative apparatus and method designed for low-cost, precise, and replicable single-cell spiking (SCS). Our SCS method operates solely from capillary aspiration without the reliance on external laboratory equipment. To ensure that our method does not affect the viability of each cell, we investigated the effects of surface membrane tensions induced by aspiration. Finally, we performed affinity-based CTC isolation using human acute lymphoblastic leukemia cells (CCRF-CEM) spiked into healthy whole blood with the SCS technique. The results of the isolation experiments demonstrate the reliability of our method in generating low-concentration cell samples.

In recent years, surgical assisting robots and nursing care robots are being introduced to medical and nursing fields due to the rapid increase of the aging society. In order to assure the safety and reliability of these robots, a highly sensitive tactile sensor is necessary to detect the state of contact between these robots and human body. The target specifications of this sensor are spatial resolution of less than 1 mm, and the pressure sensitivity of less than 10 kPa. Since the tactile sensor is expected to be attached on the tip of a surgical assisting robot or the arm of a nursing care robot, it should be small and flexible and highly sensitive to contact pressure. In this study, high quality MWCNT (Multi-Wall Carbon Nanotube) has been synthesized to develop two types of tactile sensors which consisted of a MWCNT film and the area-arrayed MWCNT bundles. Thermal CVD (Chemical Vapor Deposition) method was applied to the growth of MWCNT and PDMS (Polydimethylsiloxane) was used for a flexible substrate. It was found that MWCNT bundles showed elastic deformation in the compressive strain range from 0% to 60%. The PDMS substrate showed elastic deformation under the application of bending strain of about 20%. In addition, it was confirmed that the detecting resolution of the force was lower than 1 mN, and the obtained gauge factor of the developed sensor was about 3.5.