Thermal Constraints Research Articles

Potential thermal constraints for sustainable industrial city: refined simulation and prediction of extreme urban heat island effect

Extreme urban heat islands (E-UHIs) have caused radical changes in urban climates. The lack of refined extreme thermal environment simulation models (ETESM) hinders research on the spatial evolution of E-UHI, which increases the uncertainty for managers to make sustainable decisions. Here, considering both the heating of industrial land and the heat transfer of surrounding facilities, a refined ETESM is constructed. Assuming four scenarios: maintain current trend (MtS), economically oriented scenario (EcS), environmentally oriented scenario (EnS), green transformation scenario (GtS), the future potential constraints of the E-UHI are simulated and predicted, thus determining the sustainable development decision in industrial city. The results indicate that there is significant spatial consistency between simulated temperature and actual temperature at both city scale (Pearson'r = 0.924, P<0.001) and industrial land scale (Pearson'r = 0.948, P<0.001). The average temperature under four scenarios shows: EcS>MtS>GtS>EnS. The average SST of EnS and GtS was 1.36℃ and 1.06℃ lower than that of MtS, respectively. Considering the dual constraints of environmental livability and economic sustainability, GtS is the best decision to achieve the sustainable development in industrial city. This simulation-driven study provides support for E-UHI mitigation and sustainable development of industrial city.

Phase transitions and gravitational waves in a model of ℤ3 scalar dark matter

Theories with more than one scalar field often exhibit phase transitions producing potentially detectable gravitational wave (GW) signal. In this work we study the semi-annihilating ℤ3 dark matter model, whose dark sector comprises an inert doublet and a complex singlet, and assess its prospects in future GW detectors. Without imposing limits from requirement of providing a viable dark matter candidate, i.e. taking into account only other experimental and theoretical constraints, we find that the first order phase transition in this model can be strong enough to lead to a detectable signal. However, direct detection and the dark matter thermal relic density constraint calculated with the state-of-the-art method including the impact of early kinetic decoupling, very strongly limit the parameter space of the model explaining all of dark matter and providing observable GW peak amplitude. Extending the analysis to underabundant dark matter thus reveals region with detectable GWs from a single-step or multi-step phase transition.

H3DM: A High-bandwidth High-capacity Hybrid 3D Memory Design for GPUs

Graphics Processing Units (GPUs) are widely used for modern applications with huge data sizes. However, the performance benefit of GPUs is limited by their memory capacity and bandwidth. Although GPU vendors improve memory capacity and bandwidth using 3D memory technology (HBM), many important workloads with terabytes of data still cannot fit in the provided capacity and are bound by the provided bandwidth. With a limited GPU memory capacity, programmers should handle the data movement between GPU and host memories by themselves, causing a significant programming burden. To improve programming ease, GPUs use a unified address space with the host that allows over-subscribing GPU memory, but this approach is not effective in terms of performance once GPUs encounter memory page faults. Many recent works have tried to remedy capacity and bandwidth bottlenecks using dense non-volatile memories (NVMs) and true-3D stacking. However, these works mainly focus on one bottleneck or do not provide a scalable solution that fits future requirements. In this paper, we investigate true-3D stacking of dense, low-power, and refresh-free non-volatile phase change memory (PCM) on top of state-of-the-art GPU configurations to provide higher capacity and bandwidth within the available area and power budget. The higher density and lower power consumption of PCM provide higher capacity through integrating more cells in each 3D layer and enabling stacking more layers. However, we observe that stacking more than six layers of pure-PCM memory violates the thermal constraint and severely harms the performance and power efficiency due to its higher write latency and energy. Further, it degrades the lifetime of GPU to less than one year. Utilizing a hybrid architecture that leverages the benefits of both DRAM and PCM memories has been widely studied by prior proposals; however, true-3D integration of such a hybrid memory architecture especially on top of state-of-the-art powerful GPU architecture has not been investigated yet. We experimentally demonstrate that by covering 80% of write requests in DRAM and eliminating refresh overhead, true-3D stacking of eight 32GB layers of PCM along with two 8GB layers of DRAM is possible resulting in a total of 272GB memory capacity. Based on the explored design requirements, We propose a 3D high-bandwidth high-capacity hybrid memory (H3DM) system utilizing a hybrid-3D (H3D)-aware remapping scheme to reduce expensive PCM writes to under 20% while avoiding DRAM refresh overhead. H3DM improves the performance up to 291% compared to the baseline GPU architecture while remaining within only 3% of an ideal case with DRAM-like access latency, on average. Moreover, by increasing the dataset size above the baseline GPU memory space, H3DM improves performance and power up to 648% and 87% compared to the baseline GPU architecture since it avoids expensive data transfers through off-chip communication links.

Multi-Physics Three-Dimensional Component Placement and Routing Optimization Using Geometric Projection

Abstract This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), heating, ventilation, air-conditioning, and refrigeration, and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions and are presented to exhibit the capabilities of the proposed 3D multi-physics spatial packaging optimization framework.

New electronics for the HADES MDC drift chambers

The drift chambers of the HADES experiment at SIS-18 at GSI, Darmstadt/Germany, form the main tracking system of the spectrometer. Designed more than twenty years ago, the whole front-end electronics chain is being replaced with state-of-the-art electronics to cope with the increasing failure rate of the old electronics and with advanced requirements of the experiment, e.g. the trigger rate.The new analog signal processing is based on the PASTTREC ASIC, developed for the Straw Tube Tracker of the PANDA Experiment. The digitization of data happens in FPGA-based TDCs.The main challenges of the project are the strict spatial constraints given by the experiment setup to place the front-end boards and the noise sensitivity of the large area gas detectors. In addition, the power consumption needed to be kept low due to thermal constraints.

A multi-setpoint cooling control approach for air-cooled data centers using the deep Q-network algorithm

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints.

Effectiveness of Maxwell velocity and smoluchowski thermal slip constraints in presence of non-uniform heat source

Magnetohydrodynamic flow of nanomaterial by a stretched boundary is addressed. Entropy generation is examined. Variable fluid characteristics are considered. Buongiorno thermal enhancement model in presence of Darcy-Forchheimer expression is addressed. Nonlinear thermal radiation, non-uniform heat source and dissipation are considered in thermal expression. Slip conditions in chemically reactive flow are addressed. Here Maxwell slip velocity and Smoluchowski slip temperature are under consideration. Impact of Arrhenius activation energy is deliberated. Optimal homotopy analysis approach (OHAM) is utilized for construction of convergent solutions. Graphical description for liquid motion, entropy rate, concentration and temperature are examined. Higher magnetic variable has reverse effect on entropy rate and velocity. Higher temperature and entropy rate are seen for radiation parameter. Higher temperature dependent conductivity parameter has opposite effect on liquid motion and entropy rate. Liquid flow has same impact for both porosity and velocity slip parameters. Larger thermal slip variable lead to decrease thermal distribution while reverse impact holds for heat source. Concentration has opposite trend through both random and thermophoresis parameters. An enhancement in thermal distribution and entropy rate are seen through variable thermal conductivity. An intensification in entropy rate has been noticed through concentration dependent conductivity variable. The considered configuration has relevance for detecting cooling towers, polymer extrusion, spinning of filaments, cable coating and metallurgical processes.

Effect of 241Am buildup during spent fuel cooling on the decay heat of vitrified high-level waste and post-closure safety assessment

Long-term interim storage of spent fuel is a key factor affecting the thermal properties of vitrified high-level waste (HLW). In this study, we observed that increasing the spent fuel cooling-off period from 4 to 100 years decreased the decay heat of vitrified HLW and allowed a volume reduction of up to 13% based on the loading of the waste up to the thermal constraint of the repository. Contrastingly, the glass surface temperature 1000 years after disposal and the temperature-dependent glass dissolution rate increased with the cooling-off period due to 241Am buildup from 241Pu within that period. However, these dissolution rates can be regarded as nearly equivalent throughout the lifetime of glass dissolution because the glass temperature approached the host rock temperature and remained at this temperature for most of its lifetime. Consequently, the impact of 241Am buildup was confirmed to be negligible in the safety assessment of geological disposal with an intrinsically large safety margin.Graphical abstractPlots illustrating the effect of spent fuel cooling-off period on the thermal properties of vitrified high-level waste (HLW) and its long-term disposal safety. The left shows the results of waste volume reduction by higher waste loadings in the context of thermal constraint of the bentonite buffer less than 100 °C. The center shows the dissolution rate after disposal depending on the temperature of glass surface. The right is the result of safety assessment for different dissolution rates.

Residual Stresses in a Wire and Arc-Directed Energy-Deposited Al–6Cu–Mn (ER2319) Alloy Determined by Energy-Dispersive High-Energy X-ray Diffraction

In order to enable and promote the adoption of novel material processing technologies, a comprehensive understanding of the residual stresses present in structural components is required. The intrinsically high energy input and complex thermal cycle during arc-based additive manufacturing typically translate into non-negligible residual stresses. This study focuses on the quantitative evaluation of residual stresses in an Al–6Cu–Mn alloy fabricated by wire and arc-directed energy deposition. Thin, single-track aluminum specimens that differ in their respective height are investigated by means of energy-dispersive high-energy X-ray diffraction. The aim is to assess the build-up of stresses upon consecutive layer deposition. Stresses are evaluated along the specimen build direction as well as with respect to the lateral position within the component. The residual stress evolution suggests that the most critical region of the specimen is close to the substrate, where high tensile stresses close to the material’s yield strength prevail. The presence of these stresses is due to the most pronounced thermal gradients and mechanical constraints in this region.

Research on energy redistribution of hybrid vehicle considering thermal constraints

Thermal management is one of the key factors affecting the performance of hybrid vehicles. However, most traditional energy management strategies lack the consideration of thermal constraints under harsh operating conditions. In this paper, taking a specific type of diesel-electric hybrid system as the research object, a strategy for energy redistribution under thermal management constraints is proposed. The diesel-electric hybrid system model is built and verified. Based on the model, the cooling capacity indexes are selected, and the weight coefficient of each cooling capacity index is determined by neighborhood component analysis. Then a comprehensive thermal evaluation system is obtained. Combined with the thermal evaluation system, the energy redistribution strategy based on the momentum gradient descent method is proposed. Two typical working conditions of high altitude and battery cooling system deterioration are selected, and the energy redistribution strategy is simulated and analyzed in real-time under both working conditions. The results show that the energy redistribution strategy can significantly improve the thermal state of the system at the expense of less overall energy consumption, take into account the economy and thermal balance, and ensure the reliable operation of the vehicle.

Thermal constraints on exercise and metabolic performance do not explain the use of dormancy as an overwintering strategy in the cunner (Tautogolabrus adspersus).

Winter cold slows ectotherm physiology, potentially constraining activities and ecological opportunities at poleward latitudes. Yet, many fishes are winter-active, facilitated by thermal compensation that improves cold performance. Conversely, winter-dormant fishes (e.g. cunner, Tautogolabrus adspersus) become inactive and non-feeding overwinter. Why are certain fishes winter-dormant? We hypothesized that winter dormancy is an adaptive behavioural response arising in poleward species that tolerate severe, uncompensated constraints of cold on their physiological performance. We predicted that below their dormancy threshold of 7--8°C, exercise and metabolic performance of cunner are greatly decreased, even after acclimation (i.e. shows above-normal, uncompensated thermal sensitivity, Q10>1-3). We measured multiple key performance metrics (e.g. C-start maximum velocity, chase swimming speed, aerobic scope) in cunner after acute exposure to 26-2°C (3°C intervals using 14°C-acclimated fish) or acclimation (5-8 weeks) to 14-2°C (3°C intervals bracketing the dormancy threshold). Performance declined with cooling, and the acute Q10 of all six performance rate metrics was significantly greater below the dormancy threshold temperature (Q10,acute8-2°C=1.5-4.9, mean=3.3) than above (Q10,acute14-8°C=1.1-1.9, mean=1.5), inferring a cold constraint. However, 2°C acclimation (temporally more relevant to seasonal cooling) improved performance, abolishing the acute constraint (Q10,acclimated8-2°C=1.4-3.0, mean=2.0; also cf. Q10,acclimated14-8°C=1.2-2.9, mean=1.7). Thus, dormant cunner show partial cold-compensation of exercise and metabolic performance, similar to winter-active species. However, responsiveness to C-start stimuli was greatly cold-constrained even following acclimation, suggesting dormancy involves sensory limitation. Thermal constraints on metabolic and exercise physiology are not significant drivers of winter dormancy in cunner. In fact, compensatory plasticity at frigid temperatures is retained even in a dormant fish.

ANALYSIS OF THERMAL CONSTRAINTS WITH BIOHEAT TRANSFER IN HUMAN SKIN TISSUES

Heat transfer exchanges energy with the temperature difference between the physical systems. Similarly, bioheat plays an important role in living systems. It discusses changes in the thermal energy of living systems. This paper presents a mathematical model describing the process of heat transfer in the skin tissues of the human body during infrared sauna therapy that provides valuable insights into the thermal consequences of this treatment. The two different cases of heat generated during infrared sauna therapy vary exponentially and linearly have been discussed. The mathematical model of this process has been solved by using the reduced differential transform method. An analytical solution of this model contains the thermal stresses and displacement formed during the therapy. Temperature distribution and linear thermoelastic results on skin tissues during sauna treatment have been studied and their different effects have been discussed graphically. This knowledge contributes to the security and effectiveness of infrared sauna treatment.

ECR Spotlight – Lauren Rowsey

ECR Spotlight is a series of interviews with early-career authors from a selection of papers published in Journal of Experimental Biology and aims to promote not only the diversity of early-career researchers (ECRs) working in experimental biology but also the huge variety of animals and physiological systems that are essential for the ‘comparative’ approach. Lauren Rowsey is an author on ‘ Thermal constraints on exercise and metabolic performance do not explain the use of dormancy as an overwintering strategy in the cunner (Tautogolabrus adspersus)’, published in JEB. Lauren is a PhD candidate in the lab of Ben Speers-Roesch at University of New Brunswick Saint John, Canada, investigating physiological performance of fishes in challenging environments.

Electromagnetic Absorbing Materials Design by Optimization Using a Machine Learning Approach

One of the state-of-the-art strategies to decrease the isolation between sections of personal digital assistance devices, taking into account the unavoidable thermal, mechanical, weight, and manufacturability constraints is the proper introduction of electromagnetic (EM) absorbing materials. The global target of this study is the optimized synthesis of the EM properties of a material using a machine learning approach. The sought material must improve the shielding effectiveness between two regions of a device. The first part of this paper is dedicated to the assessment of the performances of an artificial neural network used as function approximation to compute the values of the shielding without resorting to complex and time-consuming full-wave numerical simulations. The network is assessed with respect to either the variations of the training set and algorithm or of its architecture. Significant figures of merit are introduced. The second part is devoted to the description and definition of nature-inspired algorithms demanded to manage the optimization problem.

Assessing and comparing a DDPG model and GA optimization for a heat and power virtual power plant operating in a power purchase agreement scheme

This paper proposes a deep deterministic policy gradient (DDPG) model for the operation management of a solar power-based virtual power plant (VPP) having a PPA with the grid and supplying power and thermal energy to consumers. The VPP serves to balance the solar power intermittency, cover the demand whenever solar power is absent, and ensure an efficient supply of energy. The literature in this field has introduced optimization algorithms to determine the power plant's output power or heat on a rolling-horizon basis. Using the function approximation category, which involves reinforcement learning with neural networks, to solve the simultaneous thermal and power operation management of VPPs is still not well developed. The challenges imposed in this model are sourced from the non-linearity of the CCHP, the power and thermal balance constraints, and the consideration of continuous variables rather than discrete ones. A case study is simulated in Egypt to assess and compare the models. Compared to the genetic algorithm optimization, the proposed DDPG model achieved 3% more profit, 12% higher carbon dioxide (CO2) emissions, and 9% lower natural gas consumption. The DDPG solution was 57% faster than the GA. The results of the DDPG model proved that machine learning methods could outperform optimization in terms of optimality achievement and speed of solution. The DDPG improved the operation of energy storage units and was able to recognize the supply-demand operational pattern, ensuring the scalability of the VPP to cope with different energy demand levels.

Role of continental margin architecture in models of ophiolite emplacement

Ophiolite obduction, the process during which part of the oceanic crust overlaps the continental margin, presents a challenge in geodynamic reconstructions of lithospheric processes. The difference in buoyancy between the dense oceanic crust and the relatively buoyant continental crust makes obduction of the oceanic crust difficult, if not impossible, if we only consider the buoyancy forces. The initial configuration of the oceanic basins must have specific thermal and geometric constraints to overcome the difficulties posed by the negative buoyancy. Here, we present a systematic investigation of the geometric and geodynamic parameters controlling the process of ophiolite emplacement. We show which parameters are the most important during ophiolite emplacement and the optimum geometries favouring this emplacement. We focus on ‘Tethyan’ ophiolites, which are characterized by a relatively small inferred basin size and are commonly found in the Mediterranean region. Based on a combination of parameters, we identify the configurations most susceptible to ophiolite obduction. Our models, in agreement with the geological data, show that to achieve ophiolite obduction, the obducted lithosphere must be young and the length of the ocean–continent transition zone must be relatively sharp. Supplementary material: Supplementary figures S1–S5, that are mentioned in the main text, are available at https://doi.org/10.6084/m9.figshare.c.6922526 Thematic collection: This article is part of the Ophiolites, melanges and blueschists collection available at: https://www.lyellcollection.org/topic/collections/ophiolites-melanges-and-blueschists

Multi-Objective Genetic Algorithm Optimization of Thermal Limit Parameters for Low-Frequency Oscillation Control in Power Systems

This work presents the optimization of thermal limit parameters constraint using a multi-objective genetic algorithm. By optimizing the constraint parameters of the formulated objective function, the power transfer capabilities of the system can be maximized, thereby reducing the chances of oscillations in the power system. The study involves formulating an objective function and constraints, implemented in MATLAB Simulink. Initialization of generation fitness and optimal mean value precedes computation of fitness function and mean value. Fitness values are compared against predefined benchmarks to gauge optimization levels, with verification ensuring mean fitness meets a predetermined threshold. Achieving the threshold to 1.05706 resulted to better power transfer efficiency by decreasing oscillations that previously impacted power stability. Optimized thermal limit parameters enhance power transfer capabilities, minimizing oscillations, ensuring stable operation, and reducing the risk of system instability.

A multi-layer flexible photothermal titanium nitride-based superhydrophobic surface for highly efficient anti-icing and de-icing.

Ice accumulation presents a significant challenge for various residential activities and industrial facilities. Most current de-icing methods are time-consuming and costly. Photothermal superhydrophobic surfaces have garnered significant attention in the field of anti-icing and de-icing due to their environmentally friendly and energy-saving characteristics. However, obtaining photothermal superhydrophobic surfaces with both reliable icing delay and effective photothermal de-icing capabilities at ultra-low temperatures (<-30 °C) remains significantly challenging. In this study, we prepared a multilayer flexible photothermal TiN-based superhydrophobic surface (ML-SHS), comprising an FAS@SiO2/TiN superhydrophobic layer and a PDMS/Triton X-100 flexible supporting layer. The optimal ML-SHS exhibits excellent superhydrophobicity (a water contact angle of 162.7° and a sliding angle of 2°) and an average light absorption of 95.6%, and generates a substantial surface temperature increase of 80.2 °C under 1 sun illumination. Droplets easily roll off the ML-SHS at -10 °C without solar illumination and at -35 °C under 1 sun illumination, demonstrating excellent passive anti-icing capability. Due to its excellent photothermal conversion and thermal constraint capabilities, the accumulated ice layer on the ML-SHS rapidly melts within 450 seconds at -20 °C under 1 sun illumination. The ML-SHS also possesses self-cleaning properties, mechanical durability, and chemical stability, ensuring the usability of the superhydrophobic surface under harsh conditions. Our study may offer a novel approach for the design and fabrication of photothermal superhydrophobic surfaces, facilitating efficient passive anti-icing and active de-icing in practical applications.

Model Predictive Control in buildings with thermal and visual comfort constraints

Model Predictive Control can cope with conflicting control objectives in building energy managements. In terms of user satisfaction, visual comfort has been proven in several studies to be a crucial factor, however thermal comfort is typically considered the only important aspect. Besides human well-being, visual comfort strongly impacts the productivity of the occupants in offices. Therefore, from an economic point of view, it is essential to include visual comfort in Model Predictive Control for buildings. In this paper semi-linear support vector machine is applied to learn suitable models for visual comfort measured by Daylight Glare Probability. The resulting model is incorporated into a Model Predictive Control framework, together with an autoregressive exogenous model accounting for the thermal dynamics of the building. The approach is validated through an extensive numerical case study, and the benefits of including visual comfort and blind control in the Model Predictive Control problem are evaluated. We observe that the proposed Model Predictive Control scheme ensures both the thermal and visual comfort constraints at the expense of 2.2% to 7.2% higher energy consumption compared to the benchmark Model Predictive Control configuration, which considers only the thermal comfort constraints.

Real-time multi-variable optimization of the interstage subcooling vapor-injection based transcritical CO2 HPWH: An integrated model predictive control approach

The interstage subcooling vapor-injection technique has been introduced to transcritical CO2 heat pumps to alleviate performance degradation under conditions of low ambient temperature and high water-outlet temperature. This type of solution holds considerable appeal and finds utility across a wide range of applications. However, the efficacy and efficiency of such systems are contingent upon their management, which frequently requires the implementation of suitable control systems. The primary objective is to enhance the overall system operational performance, resulting in tangible advantages in terms of economic viability, energy conservation, and environmental sustainability. To achieve these objectives, one can utilize both explicit and implicit optimization methods ranging from Model Predictive Control (MPC) to Extremum Seeking Control (ESC), each with its strengths and weaknesses. This paper compares the performance of an ad hoc integrated MPC strategy and a multi-variable ESC one. Based on the results of the system operation employing the latter strategy, a significant linear correlation was discovered between the optimal medium pressure and the optimal discharge and suction pressure. Consequently, an intrinsic self-optimization strategy for the medium pressure was developed. Subsequently, a dynamic system identification was performed on the new system, incorporating this proposed strategy. To optimize the system Coefficient of Performance (COP) while maintaining the water outlet temperature, ensuring the thermal load constraint, the MPC strategy was employed. The main focus of the MPC was to determine the optimal Electronic Expansion Valve (EEV) opening degree and water pump settings. The integrated MPC strategy was evaluated and compared to the multi-variable ESC strategy under various conditions, including fixed design conditions, changing ambient conditions, and step-change water outlet temperature. The results clearly demonstrated the effectiveness and superiority of the integrated MPC.