Guangzhou Design Institute

The integrated pump-gate is a hydraulic facility that integrates a pumping station and a gate, playing a vital role in urban drainage systems, flood control, and other scenarios. Although integrated pump-gates are widely used, their internal flow presents different forms depending on the application scenarios, such as backflow, vortices, and cavitation. These effects markedly influence the pump’s hydraulic performance, operational stability, and overall reliability. This study investigates the cavitation characteristics and internal flow fields within the complex geometry of the integrated pump-gate and numerically simulates the cavitation phenomenon using the SST turbulence model. Specifically, the influence of the impeller, guide vanes, and structural supports on the cavitation performance and internal flow state was analyzed. The results show that the geometric characteristics of the impeller’s leading edge significantly influence the cavitation structure. Regarding cavitation performance, NPSHc was determined to be 5.3 m. At the leading edge of the guide vanes, cavitation usually occurs at the axial diffusion position of the flow channel, and the degree of cavitation is affected by the relative position of the guide vanes and the impeller blades. The structural supports and protrusions significantly affect the vortex structures in the flow field, with protrusion-induced vortex clusters dominating the guide vane region.

Health Engineering (HE) has significantly advanced objective health metrics, yet often overlooks the subjective, value-laden dimension of Quality of Life (QoL), creating a gap in achieving genuine human flourishing. This paper addresses this limitation by introducing a Dual-Dimension QoL Model (DD-QoL), which integrates the Objective Functional Dimension (OFD) with the Subjective Value Dimension (SVD). Based on this model, we develop a Value-Sensitive Assessment Framework (VSAF), a distinctive methodological contribution that uses conceptual engineering to shift HE’s primary goal from merely maximizing OFD to optimizing holistic QoL. Through an illustrative example of an AI-assisted care system for Alzheimer’s disease, we qualitatively demonstrate how a VSAF-guided approach can enhance SVD outcomes without compromising OFD. Our primary contribution is a novel, philosophically-grounded framework that provides a supplementary approach for reconstructing HE goals, ensuring that technological progress serves the broader, more profound aim of promoting valuable life experiences

The high energy consumption and substantial electricity costs of cloud data centers pose significant challenges related to carbon emissions and operational expenses for service providers. The temporal variability of electricity pricing in real-world scenarios adds complexity to this problem while simultaneously offering novel opportunities for mitigation. This study addresses the task scheduling optimization problem under time-of-use pricing conditions in cloud computing environments by proposing an innovative task scheduling approach. To balance the three competing objectives of electricity cost, energy consumption, and task delay, we formulate a price-aware, multi-objective task scheduling optimization problem and establish a Markov decision process model. By integrating prioritized experience replay with a multi-objective preference vector selection mechanism, we design a dynamic, multi-objective deep reinforcement learning algorithm named TEPTS. The simulation results demonstrate that TEPTS achieves superior convergence and diversity compared to three other multi-objective optimization methods while exhibiting excellent scalability across varying test durations and system workload intensities. Specifically, under the TOU pricing scenario, the task migration rate during peak periods exceeds 33.90%, achieving a 13.89% to 36.89% reduction in energy consumption and a 14.09% to 45.33% reduction in electricity costs.

Buildings are under increasing pressure to address decarbonization and climate adaptation, which is pushing design practice from post hoc performance checks to performance-driven generative design (PDGD). This review maps the current state of PDGD in buildings and proposes an engineering-oriented framework that links research methods to deployable workflows. Using a PRISMA-based systematic search, we identify 153 core studies and code them along five dimensions: design objects and scales, objectives and metrics, algorithms and tools, workflows, and data and validation. The corpus shows a strong focus on facades, envelopes, and single-building massing, dominated by energy, daylight and thermal comfort objectives, and a widespread reliance on parametric platforms connected to performance simulation software with multi-objective optimization. From this evidence we extract three typical workflow routes: parametric evolutionary multi-objective optimization, surrogate or Bayesian optimization, and data- or model-driven generation. Persistent weaknesses include fragmented metric conventions, limited cross-case or field validation, and risks to reproducibility. In response, we propose a harmonized objective–metric system, an evidence pyramid for PDGD, and a reproducibility checklist with practical guidance, which together aim to make PDGD workflows more comparable, auditable, and transferable for design practice.

Vibration control for over-track structures is a key challenge in urban rail transit. To systematically investigate the determining effects of building height and train speed on dynamic response, this study developed a novel moving excitation system. Unlike conventional fixed-point or shaking table methods, this system faithfully reproduces the spatio-temporal “scanning effect” of train loads. In conjunction with a 1:20 modular scaled physical model, a systematic experimental investigation was conducted on structures of different heights (2, 5, 8, 11, and 15 stories) under various train speeds (60, 80, and 100 km/h), with an experimental uncertainty controlled within ±6%. The results revealed two distinct patterns: low-rise rigid structures (≤5 stories) exhibited a monotonic amplification of vibration (top-floor response amplified by 13–28%), whereas mid-to-high-rise flexible structures (≥8 stories) displayed an “attenuation-followed-by-amplification” pattern, with mid-height vibration levels reduced by over 50%. This transition is attributed to a shift in structural dynamics, as the fundamental frequency decreases from approximately 230 Hz (2-story) to approximately 100 Hz (15-story). Furthermore, linear regression analysis (R2 > 0.93) confirmed that while train speed linearly scales the response amplitude, the distribution pattern is strictly dictated by the structure’s intrinsic low-order modes. These findings provide a quantified theoretical basis for vibration mitigation in over-track developments.

ABSTRACT With the acceleration of urbanization, the treatment of municipal solid waste (MSW) has become one of the global challenges. The waste incineration process will produce fly ash containing a large amount of heavy metals and organic matter. The aim of this work is to investigate the physicochemical properties of fly ash and its roasting mechanism to provide a scientific basis for the safe handling and resource utilization of fly ash. Through the systematic analysis of fly ash samples from several waste incineration power plants of Guangzhou Environmental Investment Co., it was found that the physicochemical properties of fly ash are affected by various factors, including fuel type, combustion mode, furnace structure, fly ash temperature, hygroscopicity, etc. Through laser particle size analysis, XRD, XRF, and other technical means, this study characterizes the physicochemical properties of fly ash in detail and investigates how these properties affect the deposition behavior and slaking characteristics of fly ash in the flue. The research results provide a scientific basis and technical support for preventing and solving the problem of ash plugging in the flue gas of boilers, thus promoting the continuous progress and development of waste incineration power plants generation technology.

We propose a Physics-Enhanced Graph Attention Network (PE-GAT) to mitigate the prediction instability and physical inconsistencies prevalent in power grids with high renewable penetration. This framework orchestrates spatial topology awareness, physical knowledge embedding, and safety constraints within a unified architecture. Specifically, a three-dimensional encoding mechanism synthesizes geographic, electrical, and topological attributes to capture heterogeneous node dependencies.At the physical level, a power transfer distribution factor (PTDF) is embedded in the graph attention structure and constraint functions to ensure that the prediction results conform to power flow laws. At the optimization level, a safety-oriented composite loss function is designed to enhance the model's sensitivity and stability to high-load and overload conditions. Experimental results based on a typical power grid scenario in Guangzhou demonstrate that PE-GAT achieves high-precision prediction performance with an RMSE of 0.01493 and a MAPE of 2.80%. It effectively captures the spatiotemporal coupling characteristics of the power grid while maintaining good physical consistency and safety sensitivity. This study provides a reliable and explainable modeling approach for real-time situational awareness, load risk assessment and safe scheduling decisions of smart grids.