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The launcher mirrors will allow the injection of microwave power from the gyrotrons in the plasma of the Divertor Tokamak Test facility, under construction in Italy. The active cooling of the elliptical M1 (curved and not-steerable) and M2 (flat and steerable) mirrors, both subject to a significant heat load, is investigated in this article. Several cooling solutions are analyzed: a spiral cooling for M1, both in a massive and in a lightened configuration, and also a radial-azimuthal cooling for M2, both in a massive and in a lightened configuration. Figures of merit are computed for the different configurations.

This paper proposes an efficient thermoelectric temperature control system based on an improved proportional integral differential algorithm in which energy feedback technology is used to enhance thermoelectric cooling. In the proposed power management system, two groups of batteries are efficiently and alternatingly charged and discharged such that the information of the circuit can be monitored in real time. The PID algorithm is improved by using the idea of a state machine to control the thermoelectric coolers through an H-bridge circuit with pulse-width modulation. Finally, the energy feedback circuit combined with improved synchronous switching technology is designed to recycle the energy to drive the sensor. By inputting current of 3.1A, a wide range of temperature control from 1.437 to 60.187 was implemented. While targeting a temperature of 10 at an ambient temperature of 22, the proposed temperature control system had a control time of 30.5s, compared with 287s when using the conventional method, with an accuracy of 0.1, and an error of only 0.35. The results confirm that electric energy at a peak voltage of 1.2V and current of 24A can be recovered. The proposed energy feedback system can thus improve the efficiency of energy utilization of TEC from peripheral circuits.

In this paper, we present an adaptive Reinforcement Learning (RL) agent training approach which aims to provide a temperature control adaptable to various types of buildings. The main purpose of the proposed method is to avoid repeating the training of RL agents on every new building and therefore skip the modeling part and ease the spread of RL-based controllers. This study includes analysis of the proposed method working along with ACKTR, a state of the art RL algorithm, regarding parameter tuning, algorithm convergence, consumption reduction and state observation accuracy. The RL based controller is applied first to single rooms and then extended to entire buildings, to demonstrate its generalization efficiency. To measure the performance of the RL controller, comparisons with MPC and ON/OFF based controllers are performed. On each test, the adaptive RL temperature control was similar to MPC and better than ON/OFF control, while being able to reach up to 5% of energy savings compared to MPC and 10% compared to ON/OFF control.

Adaptive temperature control is proposed to increase the nonthermal exposure level of in vitro experiments. By regulating an air temperature to cancel the exposure-induced temperature elevation in cells, the maximum specific absorption rate (SAR) is at least doubled to trigger and characterize nonthermal effects. The air temperature is prescribed by an iteration algorithm to balance the maximum and minimum cell temperature levels while preventing them from exceeding a nonthermal threshold. Adaptive temperature control is applied to a 900-MHz exposure setup based on a TEM cavity. An air temperature regulator was installed to the TEM cavity loaded with cell monolayers in two 35-mm-diameter Petri dishes. SAR and temperature distributions in the culture medium were calculated by the finite-difference time-domain algorithm and validated by | S₁₁| and temperature measurements. Using a properly regulated air temperature, adaptive temperature control suppressed the temperature variances in cells within the 0.1 °C threshold through a 60-min exposure with a 6.10-W/kg average SAR. The SAR is 2.43 times as much as that of conventional temperature control using a stable air temperature. An air temperature regulator for adaptive temperature control is a simple addition to upgrade a conventional exposure setup and increase the exposure level in search of nonthermal effects.

Active disturbance rejection controller (ADRC) has achieved soaring success in motion controls featured by rapid dynamics. However, it turns obstreperous to implement it in the power plant process with considerable time-delay, largely because of the tuning difficulty. To this end, this article proposes a quantitative tuning rule for the time-delayed ADRC (TD-ADRC) structure based on the typical first order plus time delay (FOPTD) model. By compensating the FOPTD process as an integrator plus time delay in low frequencies, the gain parameter of TD-ADRC can be related to a scaled time constant which shapes the closed-loop tracking performance. Bandwidth parameter of extended state observer is scaled as a dimensionless parameter. A sufficient stability condition of TD-ADRC is theoretically derived in terms of the scaled parameter pair, the range of which falls within the practical interest. Relative delay margin is revealed as a critical robustness metric among others, a default pair of scaled parameter setting is recommended as well as an explicit retuning guideline according to the user's preference for performance or robustness. Simulation and laboratory water tank experiment validate the tuning efficacy and a coal mill temperature control test depicts a promising prospective of the proposed method in process control practice.

A gel card incubator is a significant clinical interface device that brings the gel card’s temperature up, and maintains it at body temperature, and completes the initial step in determining the blood grouping. Achieving a much faster closed-loop response to shorten the warm-up period and maintaining a uniform temperature inside the incubator are crucial requirements for medical incubation devices. To accomplish the incubator control requirements and deal with dead time, this study proposes a novel nonlinear plus integral control (NPI) scheme coupled with a time-delay compensator using an incubator dynamics model. The NPI successfully shaped the control structure by utilizing a few nonlinear parameters. The control strategy, which promises significant performance by combining linear and nonlinear control actions, is easy to realize, simple to tune, and allows smooth transition between control actions. Experiments were performed in a microprocessor-controlled gel card incubator by using a computer interface to demonstrate the superiority of the proposed control technique. The comparative results prove that the proposed control scheme exhibits superior responses for transient and steady states. It also achieves successful disturbance rejection and less energy consumption than the Smith predictor-based proportional integral (PI) control.

In the extremely low-frequency electromagnetic wave communication system (ELFEWC), the power amplifier is required to output extremely low-frequency voltages, which makes the junction temperature swing of the power device significantly large. It brings great challenges to the overall reliability of the system. In this article, an active thermal control strategy based on modulated model predictive control (M2PC) is proposed for the extremely low-frequency power amplifier (ELFPA) in ELFEWC to reduce the junction temperature swing of the power device and extend the life of ELFPA. Specifically, the proposed M2PC can guarantee the output accuracy of the power amplifier with the fixed switching frequency. Furthermore, the appropriate switching frequency is selected by considering the tradeoff between junction temperature swing and total harmonic distortion to improve the reliability. The proposed control strategy has the advantages of reducing the junction temperature swing of the power device while meeting the output accuracy. Finally, simulation and experimental results of a 10kW ELFPA demonstrate the effectiveness of the proposed control strategy on the junction temperature swing reduction of the power switch.

In this article, an optimal strategy is proposed for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings to fairly ensure the occupants’ comfort, while participating in frequency regulation. With the nonlinear relationship between temperature setpoint and the fan power, the differences between indoor temperatures and temperature setpoints are considered as the objective to fairly ensure the occupants’ comfort. By the proposed strategy, the task for frequency regulation is optimally distributed according to the capacity of HVAC systems in a load aggregator. Therefore, performing frequency regulation and fairly ensuring occupants’ comfort are achieved simultaneously. Simulations on a two-area interconnected power system show that the proposed optimal strategy can improve the quality of frequency, reduce traditional generator regulation, and ensure the occupants’ comfort fairly.

The logging tools detecting the underground petroleum resources face a high-temperature downhole environment. Several passive thermal management systems have successfully protected the downhole electronic devices in previous studies but failed to exert a better temperature control performance. To solve this issue, the finite element method and the Nelder–Mead algorithm are combined to optimize the thermal design of the logging tool. The optimized logging tool is obtained after 61 optimization iterations and the maximum temperature of heat sources drops from 163.1 °C to 123 °C. Besides, phase change materials (PCMs) of the optimized logging tool store 13.7% more heat, avoiding the heat accumulation of the heat sources. An experiment was conducted to investigate the real thermal optimization effect. The maximum temperature of heat sources decreases by 30.1 °C in the optimized logging tool, and the simulated results well match the experimental results with an average error of 4.64 °C. The results verify the effectiveness of thermal optimization in logging tools, revealing the great potential of optimization algorithms in thermal design.

Lithium-ion batteries (LIBs) charging at low temperatures will easily accelerate the aging of LIBs and reduce the useful life. This paper applies advanced multi-factors coupling aging model and bi-objective particle swarm optimization (PSO) algorithm to derive suitable charging patterns for LIBs at low temperatures. Based on the results of orthogonal experiments, which consider the effects of charging rate, charging temperature and charging cut-off voltage on battery aging, the low-temperature capacity degradation model was established. Furthermore, two important yet competing charging objectives, including battery health and charging time, are taken into account simultaneously. These two conflicting charging objectives are traded off by solving a bi-objective optimization problem based on battery electrothermal and aging behavior. Combined with PSO algorithm, the optimal low-temperature charging strategy is obtained. As a result, the three-stage constant current and constant voltage (CC-CV) charging strategy is optimized to balance various combinations of charging objectives. Different tradeoffs are compared and analyzed based on the Pareto frontiers. The verification results show that the optimized three-stage CC-CV charging strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and battery health under low-temperature charging conditions.

On-platform pumping systems are a potentially critical technology for microphysiological systems (MPS) to control the pressure and flow of growth media. Supporting sufficient physiological tissue quantity in culture requires fluid flow rates on the order of microliters per second, which is larger than for typical microfluidic systems. Thus, a need exists for new types of pumping systems operating in this flow range while maintaining stringent sterility of the culture as well as enabling tight temperature control at ${37}^{\circ } {\rm C}$ . Flow rates and pressure also need to be readily computer-controlled, with a manageable set of connections into the culture incubator environment. This article describes a novel mesoscale electromagnetically driven pumping system designed to meet all these requirements. Our design achieves a very low energy dissipation of ${0.65}\,{\rm mJ}$ per switching event, which allows one pumping channel to operate at ${0.45}\,{{\rm {\mu } L} /{\rm s}}$ with an average power dissipation of 1.3 mW and ${0.04}\,{{^\circ } {\rm C}}$ temperature rise in each actuator. The actuator operates in a bistable, teeter-totter configuration with a latching force of ${4.5}\,{\rm N}$ , and a relatively large stroke of ${400}\,{{\mu } {\rm m}}$ at the actuator pole face. Preliminary operational pumping test results show the potential of this type of electromagnetic actuator for fluidic pumping. Due to their compact configuration and very high energy efficiency, these pumps can provide the foundation for multichannel, on-platform pumping for MPS platforms, as well as for a range of sterile, temperature-sensitive microflow devices such as portable, battery-operated insulin pumps.

Precise temperature control is crucial for the development of sensitive instruments, as temperature fluctuations will introduce many negative effects. In temperature control systems, $1/{f}$ noise is dominant in the low-frequency range, which limits the temperature control precision. To suppress the $1/{f}$ noise, AC bridges based on the modulation-demodulation method are commonly used. While the $1/{f}$ noise is suppressed, other types of noise rise to domination, further limiting the precision of the temperature control system. The response of the AC-bridge readout circuit to temperature fluctuation is the most serious. This paper proposes a method for suppressing the response of the AC-bridge readout circuit to temperature fluctuations. Using this method, the temperature coefficient of the readout circuit is reduced by matching the temperature coefficients of the used electro-components according to their weights of impact. This method has been verified in the test of a precise temperature control system designed for a superconducting gravimeter. The temperature coefficient of the AC-bridge circuit was reduced by approximately 57.4 times compared with the unmatched AC-bridge circuit, and the temperature fluctuation in the vacuum chamber of the superconducting gravimeter was controlled within $\pm 5~\mu \text{K}$ .

In this article, we present a new microwave system with the tunable ability of the heating region and heating effect. The heating region is changed by choosing different antennas above different heating region, which leads to larger heating range. And this system can be utilized in precise temperature control in ammonia synthesis phase. The frequency-space domain modulation technology is ingeniously suggested in our heating system. The different antennas above different heating region is then selected by changing the input working frequency. Based on the frequency-space domain modulation, we further add time domain modulation to increase the tunability of heating effect at different heating regions. By adjusting the frequency distribution of the input microwave source in time domain, where we named as time-frequency-space domain synthesis modulation method, the tunable heating region and effect is finally achieved. A simple microwave system is fabricated, and different heating effects are measured. The proposed heating method and system has benefits as following, simple structure, low cost, uniform heating, and controllable heating effect, which results in great improvement in microwave heating.

In multi-MW three-level neutral point clamped back-to-back (3L-NPC) power converters, power semiconductor devices are considered as most vulnerable components due to its thermo-mechanical fatigue stress. Particularly, rotor side converter (RSC) in doubly fed induction machine (DFIM) experiences high thermal stress during its operation around synchronous speed. It degrades the performance of power converter and reduces the lifetime availability of the drive. In practice, stabilization of temperature fluctuation across each power semiconductor device improves the efficiency and reliability of power converter. With respect to this prime aim, this paper proposes a carrier-based active thermal control method to regulate temperature fluctuation across power semiconductor devices in a multi-MW (5 x 5 MW) 3L-NPC power converters serving to a 250 MW asynchronous hydro-generating unit. To test the proposed active thermal control method, different operating conditions of a 250 MW DFIM hydro-generating unit (to be commissioned in Tehri pumped storage plant, India) are examined in MATLAB/PLECS environment. The designed active thermal control method effectively regulates mean junction temperature and temperature fluctuation of power semiconductor devices within a safe operating limit. The practical feasibility and adoptability of proposed active thermal control is examined through a 2.2-kW DFIM laboratory prototype.

This article proposes a current injection-based simultaneous stator winding and permanent magnet (PM) temperature tracking technique for dual three-phase permanent magnet synchronous machines (PMSMs). The estimation models involving winding and PM temperatures are derived for different control strategies with the cancelation of inductances. To improve the tracking performance, a Kalman filter is used for simultaneous winding and PM temperature estimation. Compared with the existing methods, the proposed simultaneous winding and PM temperature estimation technique is independent of the voltage-source nonlinearity effect and is applicable to various machine operating conditions. The main features of the proposed method are underscored by the cancelation of inductance and the elimination of the need for a lookup table. The test results on a laboratory dual three-phase PMSM are used to validate the proposed method under different speed and torque conditions.

We present a large-signal SPICE model for the depletion-type silicon ring (RM) modulator that includes temperature dependence. The model is based on the temperature-dependent equivalent circuit for the RM and allows easy simulation of RM modulation characteristics in SPICE. The accuracy of the model is verified with the measurement results of 25-Gb/s NRZ modulation at several different temperatures. In addition, simulation of the temperature-dependent transient responses of the RM together with the RM temperature control circuit is demonstrated in the standard IC design environment.

Ambient temperature is closely related to human health. Maintaining a comfortable and stable temperature is crucial for the treatment, rehabilitation and daily e-health care of patients, where the Internet of Things has been widely applied for this purpose. However, the existing research always separates the network design from temperature monitoring and control. In order to provide better e-health services, we design a solution to minimize the cost of energy consumption and delay. This work discusses a cyber-physical design for an indoor temperature monitoring system using a wireless sensor network. All sensors dynamically adjust the sleep/wake duty cycles and select the optimal anycast routing according to the sensed temperature. In addition, IoT-enabled heating, ventilation, and air conditioner (HVAC) systems for indoor temperature control have attracted unprecedented attention. HVAC can be connected to the Internet for weather and time-varying electricity price data. This work discusses the problem of minimizing the total energy cost of the HVAC system while keeping the indoor temperature within a prefixed range. The key idea is to leverage the time-varying electricity prices and do precooling/preheating using HVAC. The simulation results show that our temperature monitoring and control algorithm outperforms other heuristic schemes.

We present a multipoint fiber magnetic field sensing system based on magnetic fluid (MF) and frequency-shifted interferometry fiber loop ringdown (FSI-FLRD). This system contains a number of cascaded sensing channels, and shares a common continuous-wave light source and slow electronics instead of a pulsed light source and high-speed electron devices as required in traditional multichannel FLRD techniques. Each sensing channel is formed by incorporating an MF-coated etched fiber as the sensing head inside a fiber ringdown loop. Each sensing head is mounted on a temperature control device. We experimentally fabricated a dual-channel FSI-FLRD magnetic field sensing system. First the system’s temperature response was investigated, which indicates the MF-based sensing heads had temperature cross sensitivity. Then after keeping the temperature at 20 °C for improving testing accuracy and applying different magnetic fields, the two sensing channels were separately shown to have sensitivities of $- 9.38\times 10 ^{-5}$ and $- 2.28\times 10 ^{-5}$ /(km $\cdot $ Oe) with a linear response in the range of 221–721 Oe. The obtained sensing channel stabilities were respectively 0.02% and 0.04%, which enhance more than one order of magnitude compared with those of most conventional FLRD techniques.

Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task that requires the modeling of complex nonlinear relationships among the HVAC load, indoor temperature, and outdoor environment. This article proposes a new strategy for optimal operation of an HVAC system in a commercial building. The system for indoor temperature control is divided into three sub-systems, each of which is modeled using an artificial neural network (ANN). The ANNs are then interconnected and integrated into an optimization problem for temperature set-point scheduling. The problem is reformulated to determine the optimal set-points using a deterministic search algorithm. After the optimal scheduling has been initiated, the ANNs undergo online learning repeatedly, mitigating overfitting. Case studies are conducted to analyze the performance of the proposed strategy, compared to strategies with a pre-determined temperature set-point, an ideal physics-based building model, and other types of machine learning-based modeling and scheduling methods. The case study results confirm that the proposed strategy is effective in terms of the HVAC energy cost, practical applicability, and training data requirements.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-01-26, 資料番号: PA2110032000

Nanoparticles are considered important in numerous applications, therefore it is very imperative to have an easy, effective, and efficient nanoparticle synthesis technique. This work is aimed at the design and development of a portable thermal management device with features such as low-cost, accurate, easy-to-use, and miniaturized for nanoparticle synthesis and application. The main function of the proposed device is to control, manipulate, and monitor the cartridge heater and sensor respectively. The device is incorporated with a NodeMCU integrated with a PID temperature controller with a precision of ±2°C. A customized cartridge heater was used for heating purpose and a resistive temperature sensor acts as a feedback loop. A small petri dish of 50 mm (D), as a reservoir for the synthesis of ZnO nanoparticles, was kept at $70^{\circ }\text{C}$ for 3 h. The obtained results were subjected to FESEM and XRF characterization techniques to analyze surface morphology of nanoparticle samples which were obtained as flower-like shape and nanobars structure of 830nm in width. The synthesized ZnO nanoparticles were verified for electrocatalytic sensing of Dopamine. As a proof-of-principle, to check the Chemical Vapor Deposition of the synthesized ZnO nanomaterial on the bare aluminum foil of $18~\mu \text{m}$ at a small scale, without any complex measures or need of a controlled environment, is also provided. Further, this aluminum foil was used as a working electrode where cysteine was successfully sensed electrochemically. This portable thermal device can be used for carrying out several thermal-based reactions and analysis.

A Qualcomm® Hexagon™ compute digital signal processor (CDSP) enhances reliability by integrating a current and temperature limiting system to avoid circuit failures from operating at excessive current levels for a sustained duration or from thermal runaway. The current and temperature limiting system consists of on-die current and temperature sensors, a limits evaluation (LE) circuit, and a randomized pulse modulation (RPM) circuit to control the instruction-issue rate. After the on-die sensors detect current or temperature exceeding a target threshold, the RPM circuit precisely adjusts the instruction-issue rate to maximize the performance while operating below the current and temperature specifications. The RPM circuit adapts performance in approximately five CDSP clock cycles after accounting for the clock-domain-crossing synchronization overhead to satisfy the 1- $\mu \text{s}$ latency requirement for the entire limiting system. From silicon measurements in a 7-nm CDSP, the RPM instruction-issue control circuit enables a 0.4% performance resolution across a wide range of operation from 100% to 0.4%. In addition, the RPM circuit avoids thread starvation during multi-threaded execution to maintain the CDSP quality of service (QoS). In comparison to a clock control for a limiting system, the RPM instruction-issue control enables CDSP performance gains ranging from ~2% to ~50%, depending on the workload and adjustment level, while satisfying the current and temperature specifications.

Broiler feed conversion rate (FCR) optimization reduces the amount of feed, water, and electricity required to produce a mature broiler, where temperature control is one of the most influential factors. Iterative learning control (ILC) provides a potential solution given the repeated nature of the production process, as it has been especially developed for systems that make repeated executions of the same finite duration task. Dynamic neural network models provide a basis for control synthesis, as no first-principle mathematical models of the broiler growth process exist. The final FCR at slaughter is one of the primary performance parameters for broiler production, and it is minimized using a modified terminal ILC law in this article. Simulation evaluation of the new designs is undertaken using a heuristic broiler growth model based on the knowledge of a broiler application expert and experimentally on a state-of-the-art broiler house that produces approximately 40,000 broilers per batch.

The applications of Inertial Measurement Unit (IMU) based on Micro Electro Mechanical System (MEMS) are widely used because of its low-cost, small volume and low energy consumption. Unlike the accelerometers and magnetometers, which are respectively sensitive to vibrations and ferrous materials interference, MEMS type gyroscope is able to provide stable orientation information for a short period of time. Compared with these two sensors, gyroscopes are neither disturbed by vibrations nor affected by metallic materials, and therefore it would be one of the primary sensors for industrial applications. Reviewing the most recently related works, accurate attitude estimations are usually obtained by fusing gyro integration. However, it is well known that series drifts will be induced after a long-term integration. Furthermore, according to the literature and the associated experiments, they all demonstrated that MEMS type gyroscopes have strong temperature correlation because of the silicon structure. Put it simply, the temperature variations will give rise to gyro drifts severely and thereby degrade the orientation estimation precision. To address this problem, this article proposed a temperature modeling technique and a real-time drift pre-compensation procedure by applying autoregressive moving average (ARMA) technology. Different from the traditional modeling methods, the proposed method is able to describe transient drifting behaviors as well as steady state ones. Moreover, comparison studies using artificial neural networks (ANNs) are also presented. Experiments show that the proposed method provides a stable and reliable temperature-caused gyroscope drift pre-compensation. The developed method is also very suitable for the realization in a low-cost embedded system.

A new control strategy combing the Finite Element Method (FEM) with the control method is proposed to study the control of crystal growth process in this article. In the proposed control strategy, the crystal diameter control loop and the crystal temperature control loop are designed respectively to meet the requirements on crystal diameter and crystal temperature distribution. In the crystal diameter control loop, a bio-heuristic model based PID controller is designed to control crystal diameter. In the crystal temperature control loop, considering the undetectable characteristic of temperature distribution inside crystal, the control of crystal temperature distribution is transformed into the control of a key monitoring point in crystal and the constraint of crystal temperature gradient, and a model reference adaptive temperature controller is designed to achieve the control of the monitoring point temperature. Simulation results show that the crystal diameter and the temperature of the monitoring point are controlled well. Furthermore, the internal temperature distribution inside crystal is constrained within a reasonable range, avoiding the excessive thermal stress and dislocation defects. The proposed control strategy solves the problem of the traditional crystal growth system which cannot control crystal temperature, providing a new way for growing high-quality single silicon crystal.

In this article, we propose a temperature-based proportional–integral–derivative (PID) controlling algorithm using highly dense fiber Bragg grating (FBG) arrays for laser ablation (LA) of ex vivo pancreatic tissues. Custom-made highly dense FBG arrays with a spatial resolution of 1.2 mm were fabricated with the femtosecond point-by-point writing technology and optimized for LA applications. In order to obtain proper PID gain values, finite element method-based iterative simulation of different PID gains was performed. Then, the proposed algorithm, with numerically derived PID gains, was experimentally validated. In the experiments, the point temperature was controlled at different distances from the laser fiber tip (6.0, 7.2, 8.4, and 10.8 mm). The obtained results report robust controlling and correlation between controlled distance and the resulting area of ablation. The results of the work encourage further investigation of FBG array application for LA control.

Measurement of blood flow/perfusion using temperature response of a heated region of tissue has been quite well-known, where the temperature of the tissue is raised by a heating device and the temperature response is recorded by a sensor. However, the existing methods are invasive (heating element/sensor embedded in the tissue) and/or rely on the conduction heating, which is sensitive to the heating element/sensor’s contact with the tissue and the surrounding air convection. In this article, a system with a combined microwave heating (MWH) antenna and infrared (IR) temperature sensor, with closed-loop feedback control with pulse-width modulation (PWM), for noninvasive measurement of flow/perfusion, is introduced for the first time. Applying PWM is efficient and readily implementable and circumvents the need for expensive and complex power-adjustable microwave sources often used in MWH applications. A novel phantom is also introduced for the first time to be able to create quantifiable perfusion from flow circulated by a tubing system. For an MWH source with 2.1-W power at 2.4 GHz and a proportional feedback coefficient of $K_{p} = 0.88$ W/°C, the measurement results in terms of the phantom temperature read by the IR sensor and the average PWM voltage demonstrate a clear differentiation between perfusion values of 0.07 versus 0.23 mL/min/gr (0.5 °C and 0.5 V difference for temperature and average PWM voltage responses, respectively, within 2–3 min). These promising results signify the flexibility offered by the closed-loop temperature and PWM responses for low-cost evaluation of flow/perfusion using combined MWH and IR temperature sensing.

As the environmental concern is being raised over residues of lead, the trend of transferring from the conventional lead-based soldering to lead-free soldering is overwhelming. Lead-free solders require the peak temperature to be about 30 degrees Celsius higher than lead-based solders, which induce a narrower margin between the highest melting temperatures of lead-free solders and the heat-resistant temperatures of electronic components. As a result, the accuracy of temperature control of reflow systems needs to meet a higher standard to maintain the solder quality. Whereas, the conventional control process of the onboard temperature is open-loop, which cannot achieve the required accuracy. A closed-loop method by using an array of thermal image cameras for temperature monitoring is too expensive. In order to provide a low-cost and accurate temperature control solution for reflow systems, a cost-effective non-contact temperature approximation and control system is proposed in this article. The proposed temperature approximation is achieved based on a machine learning method with multiple-input single-output strategies to get a relationship between the temperatures near the PCBs and the onboard temperature. The proposed system controls the temperature in a real-time fuzzy logic algorithm to achieve a more accurate control result. The experiment results reveal the feasibility of the proposed temperature approximation and control system.

Virtual battery (VB) is an innovative method to model flexibility of building loads and effectively coordinate them with other resources at a system level. Unlike a real battery with a dedicated power conversion system for charging control, methods are required for operating building loads to deviate from the baseline to respond to grid signals. This article presents a VB control for a commercial heating, ventilation, and air conditioning (HVAC) system to follow the desired power consumption in real-time by adjusting zonal airflow rates. The proposed method consists of two parts. At the system level, a mixed feedforward and feedback control is used to estimate the desired total airflow rate. At the zone level, two priority-based algorithms are then proposed to distribute the total airflow rate to individual zones. In particular, a zonal airflow limit estimation method is proposed using machine-learning techniques, in contrast to physics-based thermal models in existing studies, to more accurately capture zonal thermal dynamics and improve temperature control performance. An office building on the Pacific Northwest National Laboratory campus is implemented in EnergyPlus, and used to illustrate and validate the proposed control.

We present evaluation of actual use of an occupancy-reactive space heating control, which changes set-point temperatures in space heating for energy-savings based on changes in occupancy state. We performed an experiment over two winter months in Lyon, France. In this experiment, occupants were provided with occupancy-reactive and pre-scheduled controls via a home energy management system (HEMS) and they were also allowed to control space heating manually via a thermostat or the HEMS. Occupants decided which control to use from among the two advanced controls and manual control, whereas control availability was limited in some experimental periods. To grasp actual usage of the two advanced controls, we introduce energy-saving potential for valve-regulated space heating and determine numbers of frequent users who applied any of the occupancy-reactive, the pre-scheduled or manual control. We also analyze actual energy consumption of space heating of the frequent users of each control. Our findings suggest energy-saving effects by the occupancy-reactive control, but the results show that the number of the occupancy-reactive control users in the experiment was not so large. This observation encourages reconsideration of the assumption that advanced controls such as the occupancy-reactive control are used fully by occupants in previous studies, indicating a necessity for promoting comprehension and active use of occupancy-reactive controls.

In the three-phase photovoltaic (PV) cascaded inverter, the output power of PV arrays is not equal due to the difference of solar radiation, temperature and other factors, which leads to the over modulation of PV cascaded inverter. In order to solve the above problems, the carrier phase-shifted PWM (CPS-PWM) control strategy based on third harmonic injection is proposed in this paper, taking a single-stage high-frequency-link isolated cascaded PV inverter topology with clamping circuits as an example. It can not only improve the utilization of DC voltage, but also enlarge the power balance constraint range between the units in the phase of cascaded PV inverter, so as to ensure that all units can avoid over modulation under some serious power imbalance cases, and will not increase the third harmonic component injection of grid current. Firstly, the topology and working principle of the proposed inverter based on CPS-PWM control strategy are introduced. Then, the improved CPS-PWM control strategy which can improve the DC voltage utilization of the PV cascaded inverter is analyzed, and the control strategy of intra-phase power balance is emphatically analyzed in the overall control strategy of PV cascaded inverter system, and it is discussed that the proposed control strategy can also enlarge the power balance constraint range between the units of the cascaded inverter. Finally, the effectiveness of the proposed PV cascaded inverter based on the improved CPS-PWM control strategy is verified by the simulation and experimental results.

Because the dual-mass MEMS gyroscope’s output is greatly influenced by temperature, which can lead to errors that cannot be ignored. To solve this problem, a novel compensation method is proposed: a parallel processing algorithm, which integrates the Permutation entropy (PE), Local Characteristic-scale Decomposition (LCD) and Adaptive network-based fuzzy inference system (ANFIS). Firstly, LCD is used to decompose the output which contains temperature noises and drifts into a trend component and several intrinsic scale components (ISC), according to autocorrelation and complexity, three different categories will be obtained by PE: pure noise output, mixed output, and drift output. The different processes are as follows, the noise output is discarded, the mixed output is filtered by SG (Savitzky-Golay filter), then dual ANFIS is applied. Since the drift output completely reflects the temperature characteristics, the degree of non-linearity is high, the ANFIS with complex rules is used for processing. And the mixed output is composed of intermediate layer modes, containing a relatively small amount of temperature characteristics, simple rule ANFIS is adopted for processing. Finally, the signal is reconstructed. After that, the temperature error experiment is carried out, the result shows the method can effectively eliminate the error and compensate for the drift, it has a fast convergence speed and good effect, and has the advantage of good compensation efficiency.

In order to improve system reliability and reduce device thermal fatigue failure in multiload wireless power transfer (WPT) systems for electric vehicles, the implementation of the junction temperature fluctuation suppression strategy for SiC MOSFETs is necessary. However, current methods are relatively lacking, and active thermal management has not been used in WPT systems. In this article, the relationship between circuit parameters and junction temperature of SiC MOSFET is analyzed. In particular, a junction temperature fluctuation tracking suppression strategy for SiC MOSFETs is proposed, which consists of a coarse and a fine adjustment stage. As for the former, a shunt capacitor bank switching method is implemented. In order to compensate for the coarse adjustment defects in the small adjustment range and poor effect, in fine adjustment stage, a changing driving voltage method is used. Finally, a 5-kW multiload WPT system is built for verification. Experimental results show that the proposed strategy has obvious effect on the suppression of junction temperature fluctuation and keeps temperature near the target temperature. Benefiting from this, the maximum 13.9 °C junction temperature fluctuation is completely eliminated when the power fluctuations are within 36.1% of the rated power, and the heat load of each SiC MOSFET can be independently adjusted.

This paper proposes the design of a state feedback Smith predictor controller (SFC-SP) for the effective temperature control in a precalciner of a cement rotary kiln. The dynamic model of this process is obtained from real-time data by applying a system identification procedure. This identification procedure yields a fourth order with dominant time delay transfer function. A state feedback controller combined with a Smith predictor is therefore designed which behaves effectively. Simulated results compare the performance of the proposed controller with a standard PID controller. Two performance indexes have been used in this comparison: the integral absolute error (IAE), and the control effort (EU). Simulations show that the proposed controller provides lower values of these indexes and, therefore, outperforms the standard PID controller in terms of performance and accuracy

Nowadays, body temperature screening is very important especially in the current global coronavirus disease (COVID-19) epidemic. It can be considered as the first step in monitoring the fever symptom if the body temperature is checked regularly. Aiming at alleviating the influence of working distance and ambient temperature disturbance, this article proposes and demonstrates a self-compensation technique that is built into our low-cost thermal imaging-based temperature screening system. The key idea relies on a combination of a 3-D depth sensor, an electronic temperature sensor, and a reference temperature for realtime data feedback and control. In addition, we obtain simple mathematical models for compensating the influence of ambient temperature and measuring distance variations. Experimental demonstration using a blackbody radiation source as an object confirms that the measured temperature under our self-compensation agrees very well with the blackbody radiation source, showing a very low standard deviation of 0.10 °C under 21.0 °C–40.0 °C ambient temperature. In addition, an improved standard deviation of 0.18 °C and a low 0.020 °C/m for the measured temperature are obtained under the variation in measuring the distance from 0.50 to 2.00 m.

Temperature control of the new-generation integrated multicore system is challenging. This is because the leakage power, which is significant in modern systems, is nonlinearly related to temperature, resulting in a complex nonlinear control problem in thermal management. In this article, a new dynamic thermal management (DTM) method with compact piecewise linear (PWL) model based predictive control is proposed to solve the nonlinear control problem. First, a compact PWL thermal model, which takes dynamic power as input, is built by combining multiple local compact linear thermal models expanded at several Taylor expansion points. These local compact linear thermal models are obtained by sampling-based model order reduction with high accuracy. Their Taylor expansion points are selected by a systematic scheme, which exploits the thermal behavior property of the multicore chips. Based on the compact PWL thermal model, a new predictive control method is proposed to compute the future power recommendation for DTM. By approximating the nonlinearity accurately with the compact PWL thermal model and being equipped with predictive control technique, the new DTM achieves an overall high quality temperature management with smooth and accurate temperature tracking. Experimental results show that the new method outperforms the linear model predictive control based method and the echo state network based predictive thermal management method in temperature management quality with lower computing overhead.

The filter design for nonlinear uncertain systems is quite challenging since efficient estimation is required against stochastic noises, nonlinear uncertain dynamics as well as their concurrent effects. To this end, this article develops a novel filter algorithm by augmenting the disturbance as well as unknown nonlinear dynamics as an extended state and constructing consistent Kalman–Bucy algorithm. The proposed extended state based Kalman–Bucy filter (KBF) is shown to be of bounded estimation error, and the estimation accuracy can be online evaluated. More importantly, the estimation of asymptotic minimum variance is realized in condition that the changing rate of uncertainty approaches to zero. Therefore, the proposed extended state filter enables effective mitigation of disturbance and unknown nonlinear dynamics in real time by feedback control. The proposed algorithm is experimentally verified via a temperature control application in proton exchange membrane fuel cell, in which the thermocouple noise and the electrochemical uncertainty are seriously presented. The temperature variation of the extended state based KBF-based control is greatly reduced, in comparison with the conventional control. The results in this article depict a promising prospect of the proposed method for industrial control applications to handle both noises and nonlinear uncertain dynamics.

In the last decades various internal refrigeration or heating equipment has been developed mainly for a specific use, such as refrigerators air conditioning and freezers each with technology for temperature control. The incubators greenhouses were originally developed to incubate long-lasting biochemical oxygen demand (BOD) tests. The objective of this work was to build a low-cost BOD incubator and to technically evaluate temperature control through an adjustment to the electrical current in the system supply in addition to wireless monitoring. The project consisted of the dimensioning and projection of the materials for the refrigeration system, the realization of the algorithm that connects the physical part with the logical part of the system, with the development of hardware and software using Arduino to the 3D mechanical part. with the construction of the incubator for the validation of the complete system. It is concluded that the use of thermoelectric TEC1-12706 was possible with the variables presented for the cooling of a small volume. Thus, the prototype of the BOD incubator achieved a minimum temperature of 13 C and maximum of 40 C with an approximate error of 0.2 C. Our BOD system presented an excellent control system through a smartphone that helps in experiments in the laboratory to guarantee the ideal temperature. In addition, the incubator is inexpensive portable and lightweight.

In order to keep a lithium-ion battery within optimal temperature range for excellent performance and long lifespan, it is necessary to have an effective control strategy for a battery thermal management system (BTMS) consisting of electric pump, cooling plate and radiator. In this paper, a control-oriented model for BTMS is established, and an intelligent model predictive control (IMPC) strategy is developed by integrating a neural network-based vehicle speed predictor and a target battery temperature adaptor based on Pareto boundaries. The strategy is applied to plug-in electric vehicles operating in electric vehicle mode. Results show its superiority in terms of battery temperature control, battery lifespan extension and energy saving. Under the new European driving cycle, average difference between the real-time battery temperature under the novel IMPC and its target temperature is 0.26 °C, and maximum temperature difference among modules is 1.03 °C. Moreover, compared with the on-off controller, model predictive control (MPC), and MPC with VSP, state of health under IMPC at the end of the driving cycle is 0.016%, 0.012%, and 0.008% higher, respectively. At this moment, the energy consumption of IMPC is 24.5% and 14.1% lower than that of the on-off controller and traditional MPC, respectively.

An ultra-sensitive mode-localized accelerometer is reported in this letter. In order to lower the coupling factor of the coupled resonators thus improving the parametric amplitude ratio sensitivity, a stiff anchor support is employed as a mechanical coupler between resonators. A coupling factor of $7.4\times 10^{-5}$ was obtained, which is so far the lowest mechanical coupling factor that has been reported for weakly coupled MEMS sensors. As a result, the proposed mode-localized accelerometer demonstrates an input-referred bias instability as high as 280ng within an integration time from 3s to 300s, and an input-referred noise floor of 250ng/ $\surd $ Hz, without utilizing temperature control or other environmental compensation, benchmarking as the highest resolution mode-localized accelerometer to-date. These metrics are comparable to state-of-the-art MEMS frequency-modulated resonant and capacitive accelerometers while exhibiting superior common-mode rejection to environmental effects.

Editor’s notes: Thermal management is a critical factor in determining the performance limits of mobile devices due to their limited cooling capabilities. This article presents a coordinated self-tuning controller that reduces thermal violations significantly while providing higher performance. — Umit Y. Ogras, Arizona State University

In order to exploit the torque capability of electrical motors, overload operations are appreciated. However, the extent and the time of the overload operation are difficult to determine. Since the capability of overload operation is closely related to the rotor temperature for some types of motors, in this article, a novel one-node lumped-parameter thermal model is proposed and adopted by the model predictive control (MPC) to achieve the active rotor temperature management. The proposed MPC control scheme can adaptively set a current limit according to the thermal state of machines to limit the rotor temperature. Compared with the existing multinode thermal model based MPC thermal control scheme, the parameters of the proposed one-node thermal model can be easily obtained from a set of experiments, while the computational burden of the proposed MPC control was decreased significantly. The proposed control scheme was assessed by simulations and experiments on laboratory machine-drive systems and compared with a proportional–integral (PI) controller based rotor temperature control. The experimental results show that the proposed scheme achieves better control performance than the conventional PI controller based thermal control and can effectively reduce the peak rotor temperature and, hence, the thermal stress of the machine for improving its lifetime.

On-chip gel actuators are potential candidates for single cell manipulation because they can realize low-invasive manipulation of various cells. We propose an on-chip gel actuator driven by light irradiation. By patterning the gel actuator with light absorber, we can control the temperature of the actuator and drive it. The proposed drive method can realize highly localized temperature control of the gel actuator and can be applied to mass integration of on-chip gel actuators. In this study, we evaluate the heat conduction of the actuator during driving and its response characteristics as a function of various design parameters. We theoretically and experimentally evaluate the response characteristics and confirm that the response characteristics can be changed by altering the size of the light absorber. Furthermore, we show some examples of cell manipulation including trapping, transport, and sorting with various sizes of light absorber. Finally, we show proof of concept for the application of the proposed drive method for massive integration of on-chip gel actuators.

Air conditioning systems have been recognized as a potential, cost-effective resource for shaping electrical load in a power system. This paper presents a new priority-based control strategy that explicitly addresses three requirements for using a collection of air conditioning units in this capacity. These are 1) tracking a regulation signal, 2) maintaining the building temperature near its set point, and 3) satisfying a cycling constraint of the air conditioners. After introducing the control strategy, we derive a bound for the regulation signal within which tracking is guaranteed while also satisfying the temperature requirement. These results are illustrated with simulations.

Many research articles are reported on humidity measurement in the literature. But there is no critical study of temperature on the response characteristics of the humidity sensors, particularly in ppm-level. Developing an experimental set up for studying the temperature effect is challenging as the variation of the ambient temperature not only varies the response of the sensor but also the humidity level inside the chamber. Commercial humidity-controlled chamber to maintain humidity and temperature is bigger in size and costly. Purposes of the present work are (i) to investigate the chip temperature effect on the characteristics of metal oxide humidity sensors in ppm level and in %RH (ii) to enhance the sensitivity of a sensor by suitably controlling its temperature in ppm level (iii) to refresh the sensor by intermittent heating pulse. A suitable experimental set up is also developed to study the temperature effect on the sensors. The chip temperature may be another method to enhance the sensitivity in low ppm moisture detection.

A virtual battery model is a simple and general method to quantify aggregate flexibility from thermostatically controlled loads (TCLs), enabling grid operators to effectively coordinate a large number of flexible building loads with supply-side resources in power systems. Lockout controls are designed to avoid wear and tear resulting from short-cycling of hardware. The lock on/off time could significantly affect aggregate flexibility from TCLs to provide ancillary services and may even fail control algorithms designed without considering the lock time constraints. This paper focuses on flexibility estimation and control design for TCLs with lock time constraints to provide frequency regulation service. We first investigate the potential impacts of lock time on TCLs’ aggregate flexibility and control performance. Both control-dependent and control-independent power bounds are then derived, based on previous TCL switching operations and regulation signals, respectively. While the control-dependent method provides aggregate flexibility for a given control method, the control-independent method calculates the theoretical maximum of power bounds. Finally, two control algorithms are proposed to better distribute flexibility over time and thereby improve signal tracking performance. The proposed methods are illustrated and validated through simulations.

Leakage power is becoming significant in new generation IC chips. As leakage power is nonlinearly related to temperature, it is challenging to manage the thermal behavior of today’s multicore systems, since thermal management becomes a nonlinear control problem. In this paper, a new predictive dynamic thermal management (DTM) method with neural network thermal model is proposed to naturally consider the inherent nonlinearity between leakage and temperature. We start with analyzing the problems of using recurrent neural network (RNN) to build the nonlinear thermal model, and point out that there is exploding gradient induced long-term dependencies problem, leading to large model prediction errors. Based on this analysis, we further propose to use echo state network (ESN), which is a special type of RNN, as the leakage-aware nonlinear thermal model. We theoretically and experimentally show that ESN achieves much higher accuracy by completely avoiding the long-term dependencies problem. On top of this nonlinear ESN thermal model, we propose a novel model predictive control (MPC) scheme called ESN MPC, which uses iterative steps to find the optimal future power recommendations for thermal management. Being able to consider the leakage-temperature nonlinear effects and equipped with advanced control technique, the new method achieves an overall high quality temperature management with smooth and accurate temperature tracking. The experimental results show the new method outperforms the state-of-the-art leakage-aware multicore DTM method in both temperature management quality and computing overhead.

The absolute gravimeter plays an important role in metrology, geophysics, and geological exploration. Seismic and environmental vibration has been one of the most serious factors limiting its performance. Consequently, an ultralow-frequency vertical vibration isolator is required to significantly improve its measurement precision. A novel active vertical vibration isolator employing geometric antispring (GAS) structure is proposed in this paper. The payload is supported by a GAS structure fixed on an inner frame, and the inner frame is hung by coil springs from the base. The relative movement of the payload with respect to the inner frame is detected, and the inner frame is driven by a voice coil actuator controlled by a feedback circuit to track the payload’s motion. The new isolator has a compact size, and it can be used for different load ranges by tuning the GAS structure. The practical closed-loop system has a resonant period of 19.2 s, compared with the period of 0.74 s in an open-loop system. Experiments showed that the new isolator has great performance in a homemade T-1 absolute gravimeter, reducing the measurement deviation by a factor of 32. It is expected to be used in both free-falling and atomic-interference absolute gravimeters. Future improvements may include optimizing the mechanical structure and integrating a temperature control subsystem.

A hybrid cryogenic permanent-magnet undulator (CPMU) with a 15-mm period length is being constructed for a TPS Phase-II beamline. A control system for this CPMU (CU15) is under development since 2018 and is based on the Experimental Physics and Industrial Control System (EPICS) architecture and Ethernet Control Automation Technology (EtherCAT) framework. The main control components include a motor with encoder for gap adjustment, corrector magnet power supplies, ion pumps and BA gauges for the vacuum system, resistance temperature detectors (RTD) and heaters for temperature control of the cryogenic environment and magnet arrays, motion safety interlock, cryogenic system and vibration monitor system. The design and implementation of the CU15 control system will be summarized in this paper.

A new control structure with constant pulling speed for growing high-quality crystal in the Czochralski (CZ) method is presented in this brief. In this control structure, the pulling speed is not involved in the controlling of crystal diameter and only the temperature is used as the control quantity. Due to the time delay and the nonlinearity relationship are commonly involved between the temperature and crystal diameter, which make the diameter control difficult and complicated, a generalized predictive controller (GPC) based on the stacked sparse autoencoder (SSAE) is proposed under the new control structure. The time delay is obtained by using the correlation identification algorithm, the input order and output order are determined by the Lipchitz quotients algorithm, and the prediction model is trained by SSAE. Combining the SSAE with the nonlinear GPC algorithm, the control law of the temperature is calculated for diameter control. The simulation result verifies the correctness of the proposed control algorithm. The experimental result indicates that the new control structure with constant pulling speed is more conducive to growing high-quality crystal, avoiding the fluctuation of pulling speed. The proposed SSAE-based GPC algorithm can accurately track the reference diameter. The studies in this brief provide a feasible strategy for growing large size and high-quality crystal in the CZ method.