A Non-Iterative Balancing Method for HVAC Duct System

Building Heating, Ventilation and Air Conditioning (HVAC) system maintain comfortable indoor environment by supplying processed air to each terminal precisely through duct system. Testing, Adjusting and Balancing (TAB) plays critical role in achieving desired air distribution. Traditional TAB method is inaccurate and inefficient due to its trail-and-error natural, which forces people to pay high but expect low. Recently, it has been proposed that non-iterative approach to TAB is promising to improve performance and reduce cost. In this paper, a novel non-iterative balancing method is developed and implemented for TAB engineers to adjust dampers systematically and efficiently. Different from other TAB methods, this method is based on modeling and optimization. The mathematical model for duct system is firstly developed from its components including fan, duct segments and dampers to predict flow rates and pressures in the duct system for any damper positions. To identify the parameters in the model, flow rate measurements are taken for each terminal on real system under different damper positions. With the obtained model, optimal damper positions that gives desired air distribution are calculated by minimizing a specific objective function. To facilitate the adjusting process in real duct system, a sequential tuning instructions are generated which can help engineers to adjust dampers to their proper position using flowmeter as indicators. In this sequential tuning process, each damper only adjusts once to reach balance. Because the pressure and airflow dynamics of the duct system has been modeled, the entire TAB procedure is deterministic and non-iterative. Simulations are performed to validate the effectiveness of this method in Matlab/Simulink environment. Comparison study with existing methods shows that the proposed TAB method significantly shorten the duration of process and reduces balancing error while using easily-accessible equipment like pressure sensor and flowmeter only. It can be expected that the TAB service contractor will apply this method for advanced duct system where accurate air distribution is strictly required

A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference. Both low-rankness and sparsity are appealing properties for the network approximation. In this paper we propose a unified framework to compress the convolutional neural networks (CNNs) by combining these two properties, while taking the nonlinear activation into consideration. Each layer in the network is approximated by the sum of a structured sparse component and a low-rank component, which is formulated as an optimization problem. Then, an extended version of alternating direction method of multipliers (ADMM) with guaranteed convergence is presented to solve the relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet and GoogLeNet with large image classification datasets. The results outperform previous work in terms of accuracy degradation, compression rate and speedup ratio. The proposed method is able to remarkably compress the model (with up to 4.9x reduction of parameters) at a cost of little loss or without loss on accuracy.Comment: 8 pages, 5 figures, 6 table

The Air Distribution Around Nozzles Based On Active Chilled Beam System

During the past two decades, the utilization of Active Chilled Beam (ACB) systems as promising air-conditioning systems has becoming increasingly prevalent in Europe, North America and Asia. Due to the advantages of energy efficient, low acoustic effect and less space requirement, ACB systems are extensively applied in offices, laboratories and hospitals. However, the studies on air distribution uniformity of ACB systems are still inadequate. The air distribution has a great impact on thermal comfort. The un-uniformity of air distribution will easily lead to turbulent flow which can cause unpleased feeling such as draft in the occupied zone. ACB terminal unit is the source of the air flow entering into the occupied place, which plays a crucial role on the air distribution inside the room. Therefore, it’s of great importance to evaluate the air distribution uniformity in the vicinity of ACB nozzles. In order to fulfil the gap, air distribution for a two-way discharge ACB terminal unit is investigated in this study. The air velocities around the nozzles under different conditions are tested in a 7.3m*3.3m*2.5m thermal isolated room and simulated by a three dimensional Computational Fluid Dynamics (CFD). After being verified, the CFD model is utilized to examine the effects of nozzle diameter and inlet pressure. From the results of experiments and simulation, it is found out that the air flow is discharged in an asymmetric way from nozzles, which is ascribed to un-uniformity of pressure distribution inside ACB caused by the layout of the duct. Moreover, the un-uniformity is significant when the nozzle diameter is large and the elevation of the inlet pressure would aggravate this un-uniformity. Therefore, as we design the ACB systems, high attention on the nozzle diameter should be paid to prevent the un-uniformity air flow when the nozzles and the inlet pressure are large. Eventually, a proper strategy to solve this problem is also proposed and validated by CFD simulation.

Condition Assessment of Bill Emerson Memorial Cable-Stayed Bridge under Postulated Design Earthquake

In this study, a three-dimensional finite element model of the Bill Emerson Memorial cable-stayed bridge was developed and validated with the acceleration data recorded during the M4.1 earthquake of May 1, 2005, in Manila, Arkansas. The model took into account the geometric nonlinear properties associated with cable sagging and soil-foundation-structure interaction. The validated model was used to evaluate the performance of a seismic protective system, the behavior of cable-stayed spans, and the accuracy of two simplified bridge models that have been extensively used by the structural control community. The calculated natural frequencies and mode shapes correlated well with the measured data. Except that the hollow columns of two H-shaped towers were near yielding immediately above their capbeams, the cable-stayed spans behaved elastically as expected under the design earthquake that was scaled up from the recorded rock motions at the bridge site. The minimum factor of safety of all cables is 2.78, which is slightly greater than the design target

Assessment of the Bill Emerson Memorial Cable-Stayed Bridge based on Seismic Instrumentation Data

In this study, both ambient and earthquake data measured from the Bill Emerson Memorial Cable-stayed Bridge are reported and analyzed. Based on the seismic instrumentation data, the vibration characteristics of the bridge are investigated and used to validate a three-dimensional Finite Element (3-D FE) model of the bridge structure. The 3-D model is rigorous and comprehensive, representing realistic dynamic behaviors of the bridge. It takes into account the geometric nonlinear properties caused by cable sagging and soil-foundation-structure interaction in the Illinois approach of the bridge. The FE model is successfully verified and validated by using the natural frequencies and mode shapes of the bridge extracted from the measured data. With the calibrated model, time history analyses were performed to assess the condition of the bridge structure under a postulated design earthquake. Since the FE model is developed according to as-built drawings, the calibrated model can be used as a benchmark for safety evaluation and health monitoring of the cable-stayed bridge in the future

Study on the Vibration and Sound Radiation Performance of Micro-Perforated Laminated Cylindrical Shells

In response to the problem of vibration and noise reduction in equipment with cylindrical shell structures, this paper focuses on the micro-perforated laminated cylindrical shell structure and establishes its finite element model. Through comparative analysis with experimental results, the reliability of the finite element modeling method is verified. Based on this, the paper places particular emphasis on the vibration and acoustic radiation performance of the structure in the 1–1000 Hz frequency range under free conditions to understand the impact of different laminated shell structures, micro-perforation parameters (porosity, aperture), sound-absorbing foam materials, and placement methods. The results indicate that micro-perforated structures can efficiently reduce the structural radiated sound power level at specific frequencies, but the overall reduction in radiated sound power level is not significant. Various types of foam are effective in reducing the structural radiation acoustic power level, with polyurethane performing best among them. Changing the location of foam placement has a relatively insignificant impact on the structural radiation acoustic power level.© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma

We present an Expectation-Maximization (EM) Regularized Deep Learning (EMReDL) model for the weakly supervised tumor segmentation. The proposed framework was tailored to glioblastoma, a type of malignant tumor characterized by its diffuse infiltration into the surrounding brain tissue, which poses significant challenge to treatment target and tumor burden estimation based on conventional structural MRI. Although physiological MRI can provide more specific information regarding tumor infiltration, the relatively low resolution hinders a precise full annotation. This has motivated us to develop a weakly supervised deep learning solution that exploits the partial labelled tumor regions. EMReDL contains two components: a physiological prior prediction model and EM-regularized segmentation model. The physiological prior prediction model exploits the physiological MRI by training a classifier to generate a physiological prior map. This map was passed to the segmentation model for regularization using the EM algorithm. We eva luated the model on a glioblastoma dataset with the available pre-operative multiparametric MRI and recurrence MRI. EMReDL was shown to effectively segment the infiltrated tumor from the partially labelled region of potential infiltration. The segmented core and infiltrated tumor showed high consistency with the tumor burden labelled by experts. The performance comparison showed that EMReDL achieved higher accuracy than published state-of-the-art models. On MR spectroscopy, the segmented region showed more aggressive features than other partial labelled region. The proposed model can be generalized to other segmentation tasks with partial labels, with the CNN architecture flexible in the framework