Morphological Adaptation for Speed Control of Pipeline Inspection Gauges MC-PIG

Passive and active hybrid pipeline inspection gauges (PIGs) have been used for in-pipe inspection. While a passive PIG cannot control its speed, the hybrid version can achieve this by using an integrated valve specifically designed and embedded in the PIG. This study proposes a generic new method for speed adaptation in PIGs (called MC-PIG) by introducing a generic, modular, controllable, external valve unit add-on for attaching to existing conventional (passive) PIGs with minimal change. The MC-PIG method is based on the principle of morphological computation with closed-loop control. It is achieved by regulating/computing the PIG's morphology (i.e., a modular rotary valve unit add-on) to control bypass flow. Adjustment of the valve angle can affect the flow rate passing through the PIG, resulting in speed regulation ability. We use numerical simulation with computational fluid dynamics (CFD) to investigate and analyze the speed of a simulated PIG with the valve unit adjusted by proportional-integral (PI) control under various in-pipe pressure conditions. Our simulation experiments are performed under different operating conditions in three pipe sizes (16″, 18″, and 22″ in diameter) to manifest the speed adaptation of the PIG with the modular valve unit add-on and PI control. Our results show that the PIG can effectively perform real-time adaptation (i.e., adjusting its valve angle) to maintain the desired speed. The valve design can be adjusted from 5 degrees (closed valve, resulting in high moving speed) to a maximum of 45 degrees (fully open valve, resulting in low moving speed). The speed of the PIG can be regulated from 0.59 m/s to 3.88 m/s in a 16″ pipe at 4.38 m/s (in-pipe fluid velocity), 2500 kPa (operating pressure), and 62 °C (operating temperature). Finally, the MC-PIG method is validated using a 3D-printed prototype in a 6″ pipe. Through the investigation, we observed that two factors influence speed adaptation; the pressure drop coefficient and friction of the PIG and pipeline. In conclusion, the results from the simulation and prototype show close characteristics with an acceptable error.

Towards multifunctional building elements: thermal activation of a composite interior GFRP slab

Modular multifunctional building elements can overcome major disadvantages of the traditional sequential design and become prospective design solutions for sustainable construction. Thus, this work explores lightweight glass fiber-reinforced polymer (GFRP) profiles capabilities as multifunctional load-bearing slab modules in buildings. By adding water channels in a cellular structure of pultruded GFRP elements, hydronic radiant thermal conditioning of the indoor space can be enabled. Additionally, the water channels can protect critical slabs in case of a fire. A preliminary design of a multifunctional GFRP slab is performed for an office case study building by modifying a commercial slab profile with triangular channels. The thermal design load of the slab unit is determined using Rhino 6, and heat conduction and convective heat transfer for ceiling cooling and floor heating/cooling cases are investigated using ANSYS Fluent. The results show that a commercial GFRP profile can be modified to accommodate water channels and provide adequate heating and cooling at the upper or lower face. In addition, Serviceability Limit State is verified and required water flow adjustment in case of a fire outbreak scenario is discussed. Thus, the GFRP radiant slab has the potential as a pre-fabricated alternative for traditional embedded radiant systems.

Optimal Regulation of the Cascade Gates Group Water Diversion Project in a Flow Adjustment Period

This study focuses on the regulation demand of the cascade gates group water diversion project during the flow adjustment period. A multi-objective optimization regulation model was coupled with the one-dimensional hydrodynamic model and the multi-objective genetic algorithm. Gate opening was used as the decision variable to generate the local operation-oriented cascade gates group regulation scheme. This study considered the Shijiazhuang to Beijing section of the middle route of the South-to-North Water Diversion Project. The optimal operation model has a better control effect than the conventional control method, and the number of gate operations was reduced by 23.38%. The average water level deviation was less than 0.15 m when the feedforward control time of the cascade gates group water diversion project was not more than 24 h. The basic mechanism of maintaining water level stability during the short-term scheduling of the cascade gates group water diversion project makes use of the volume capacity, or the space of the channel pool adjacent to the water demand change position, in advance. The multi-objective optimal regulation model of the cascade gates group that was constructed in this study can quickly generate regulation schemes for different application scenarios.

Numerical analysis of hydrodynamics influenced by deformed bed due to near-bank vegetation patch

This study with a 2D hydro-morphological model analyzes hydrodynamics over flat and deformed beds with a near-bank vegetation patch. By varying the patch density, the generalized results show that the hydrodynamics over deformed beds differs a lot from those over flat beds. It is found that the deformed bed topography leads to an apparent decrease in longitudinal velocity and bed shear stress in the open region and longitudinal surface gradient for the entire vegetated reach. However, the transverse flow motion and transverse surface gradient in the region of the leading edge and trailing edge is enhanced or maintained, suggesting the strengthening of secondary flow motion. Interestingly, the deformed bed topography tends to alleviate the horizontal shear caused by the junction-interface horizontal coherent vortices, indicating that the turbulence-induced flow mixing is highly inhibited as the bed is deformed. The interior flow adjustment through the patch for the deformed bed requires a shorter distance, La, which is related to the vegetative drag length, (Cda)−1, with a logarithmic formula (La = 0.4ln[(Cda)−1] + b, with b = 3.83 and 4.03 for the deformed and flat beds). The tilting bed topographic effect in the open region accelerating the flow may account for the quick flow adjustment.

Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

In current power grids, a massive amount of power equipment raises various emerging requirements, e.g., data perception, information transmission, and real-time control. The existing cloud computing paradigm is stubborn to address issues and challenges such as rapid response and local autonomy. Microgrids contain diverse and adjustable power components, making the power system complex and difficult to optimize. The existing traditional adjusting methods are manual and centralized, which requires many human resources with expert experience. The adjustment method based on edge intelligence can effectively leverage ubiquitous computing capacities to provide distributed intelligent solutions with lots of research issues to be reckoned with. To address this challenge, we consider a power control framework combining edge computing and reinforcement learning, which makes full use of edge nodes to sense network state and control power equipment to achieve the goal of fast response and local autonomy. Additionally, we focus on the non-convergence problem of power flow calculation, and combine deep reinforcement learning and multi-agent methods to realize intelligent decisions, with designing the model such as state, action, and reward. Our method improves the efficiency and scalability compared with baseline methods. The simulation results demonstrate the effectiveness of our method with intelligent adjusting and stable operation under various conditions.

Multistage Digital-to-Analogue Chip Based on a Weighted Flow Resistance Network for Soft Actuators

A control chip with a multistage flow-rate regulation function based on the correlation between the flow resistance and flow rate has been developed in this article. Compared with the traditional proportional solenoid valve, this kind of flow valve based on microfluidic technology has the characteristics of being light-weight and having no electric drive. It solves such technical problems as how the current digital microfluidic chip can only adjust the flow switch, and the adjustment of the flow rate is difficult. To linearize the output signal, we propose a design method of weighted resistance. The output flow is controlled by a 4-bit binary pressure signal. According to the binary value of the 4-bit pressure signal at the input, the output can achieve 16-stage flow adjustment. Furthermore, we integrate the three-dimensional flow resistance network, multilayer structure microvalve, and parallel fluid network into a single chip by using 3D printing to obtain a modular flow control unit. This structure enables the microflow control signal to be converted from a digital signal to an analogue signal (DA conversion), and is suitable for microflow driving components, such as in microfluidic chip sampling systems and proportional mixing systems. In the future, we expect this device to even be used in the automatic control system of a miniature pneumatic soft actuator.

Power Flow Adjustment for Smart Microgrid Based on Edge Computing and Deep Reinforcement Learning

As one of the core components that improve transportation, generation, delivery, and electricity consumption in terms of protection and reliability, smart grid can provide full visibility and universal control of power assets and services, provide resilience to system anomalies and enable new ways to supply and trade resources in a coordinated manner. In current power grids, a large number of power supply and demand components, sensing and control devices generate lots of requirements, e.g., data perception, information transmission, business processing and real-time control, while existing centralized cloud computing paradigm is hard to address issues and challenges such as rapid response and local autonomy. Specifically, the trend of micro grid computing is one of the key challenges in smart grid, because a lot of in the power grid, diverse, adjustable supply components and more complex, optimization of difficulty is also relatively large, whereas traditional, manual, centralized methods are often dependent on expert experience, and requires a lot of manpower. Furthermore, the application of edge intelligence to power flow adjustment in smart grid is still in its infancy. In order to meet this challenge, we propose a power control framework combining edge computing and machine learning, which makes full use of edge nodes to sense network state and power control, so as to achieve the goal of fast response and local autonomy. Furthermore, we design and implement parameters such as state, action and reward by using deep reinforcement learning to make intelligent control decisions, aiming at the problem that flow calculation often does not converge. The simulation results demonstrate the effectiveness of our method with successful dynamic power flow calculating and stable operation under various power conditions.

GASTOS SOCIAIS E ACCOUNTING GIMMICKS: UMA PERCEPÇÃO DOS CICLOS POLÍTICOS ELEITORAIS NOS MUNICÍPIOS DO CENTRO-OESTE BRASILEIRO

Essa pesquisa objetivou verificar em que extensão as práticas de accounting gimmicks afetam as políticas fiscais e a execução dos gastos sociais durante os ciclos políticos eleitorais. Por meio de um modelo logit, foram analisadas as contas públicas dos 466 municípios da região centro-oeste brasileira em um painel desbalanceado no período de 2004 a 2017. Com uma especificidade singular, os municípios brasileiros têm apresentado dificuldades na manutenção de um desempenho fiscal eficiente. Uma abordagem teórica dos regimes fiscais enfatiza que a rigidez fiscal pela qual os governantes são conduzidos pode impulsionar práticas de manipulações contábeis, especialmente nos saldos do SFA (Stock-flow adjustment). Tais práticas visam melhorar os resultados orçamentários, a visibilidade política e a permanência no poder. Os achados permitiram corroborar parcialmente com a literatura de que há uma relação positiva entre as regras fiscais rígidas e as práticas de accounting gimmicks. Ainda foi possível constatar que situações de elevados índices de dependência financeira e de oportunismo político para reeleição são impulsionadores nas ocorrências dessas manipulações. Portanto, concluiu-se que a utilização dos gastos sociais, alinhados a uma abordagem teórica dos regimes fiscais rígidos e da Teoria dos Ciclos Políticos Eleitorais, é influenciada pelo comportamento oportunista dos gestores e pelas práticas de accounting gimmicks.