Statistical Energy Research Articles

Sound insulation of complex façades: A complete study combining different numerical approaches

Curtain walls present critical aspects related to noise insulation. Laboratory and field tests are traditionally the most used procedure to determine sound transmission loss. Even if they provide the best results, they are very expensive, time-consuming (at least two days) and the results are specific of the tested specimen and therefore they cannot be generalized. Conversely, traditional numerical used simulations are generally conducted through Statistical Energy Analysis (SEA) studies or analytical analysis. Both approaches do not offer reliable results as they need several laboratory tests and dedicated mathematical models.Thus, the aim of this research is to compute the curtain wall sound insulation by means of a combined simulation approach, using TMM and 3D acoustic model. The calibration and validation of this procedure is discussed in comparison with laboratory tests. Poroelastic materials as well as rigid metal stud are carefully characterized and sound source and sound field within both measured and simulated environment are calibrated and validated. Measurements are then compared with simulated transmission loss and a parametric study for mullions structure and their components is reported.Results clearly show how it is possible to completely simulate a laboratory test combining 3D simulation and Transfer Matrix Method results and how the influence of mullion components is of paramount importance for the final acoustical performances.

Statistical Energy Analysis Model for Sound Pressure Level Prediction on Refrigerators

A statistical energy analysis (SEA) model of a 510 l capacity refrigerator is used to calculate the sound pressure level generated in a reverberation room. This is a reliable indicator of the vibroacoustic performance of the refrigerator, and it is in some countries as a pre-commercialization test. The main contributions of the SEA model are the characterization of the refrigerator structure (three-layer: HIPS, polyurethane foam and steel) and the modelling of important components such as internal chambers or ventilation gratings. The simulation results are successfully compared with laboratory measurements. The SEA model is then used to understand the vibroacoustic behaviour of the refrigerator and to establish the most critical transmission paths and radiation mechanisms.

Prediction of acoustic transmission in heavily damped system using hybrid Ray-Tracing-SEA method

Classic statistical energy analysis mainly deals with the energy transmission of system with relatively low damping. With the application of passive damping treat- ments, one of the fundamental assumptions in SEA, i.e., a diffused field, tends to fail. The energy attenuation along transmission path becomes so significant that there may exist large energy level difference within one structural component. In light of this, this study proposes to use a hybrid ray-tracing-SEA method to predict the acoustic transmission in heavily damped system. Heavily damped structural compo- nents are treated as â–œcoupling elementsâ–? instead of normal â–œsubsystems.â–? The en- ergy transmission from one structural element to a connected structural element through the edge can be represented by using certain number of point sources and assuming each point source radiates certain number of acoustic rays. By tracing the travelling history of each ray, the energy attenuation along the travelling path can be achieved. With the information of energy input and energy attenuation, the equivalent coupling loss factors can be computed. By rebuilding a hybrid ray- tracing-SEA model, the energy level differences between different subsystems can be determined. Numerical validation of the ray tracing algorithm is conducted by comparing the calculated coupling loss factor with normal SEA method. Numerical study of a one-room system is given. The room is assumed to consist of six homoge- neous concrete plates and the analysis assumes no coupling between in-plan and out- plane waves. Comparisons between classic and hybrid method show that when a small number of the structural components are heavily damped, classic SEA gives similar results with the hybrid method because the prediction errors tend to cancel with each other and the transmission is dominated by paths that are not damped. With the increasing number of damped elements, SEA tends to underestimate the energy level difference.

Rashba spin-orbit interaction effects on thermal and magnetic properties of parabolic GaAs quantum dot in the presence of donor impurity under external electric and magnetic fields

Based on the effective mass approximation, the magnetic and thermal properties of parabolic GaAs quantum dot have been investigated in the presence of Rashba Spin-Orbit interaction (RSOI), donor impurity and applied magnetic and electric fields. The exact diagonalization method has been used to solve the Hamiltonian of an electron confined in a quantum dot (QD) and obtain the eigenenergies and the binding energy of the donor impurity as a function of various QD physical parameters. We have shown the dependence of the average statistical energy, magnetization, magnetic susceptibility and heat capacity of the donor impurity in the QD on: the Rashba interaction parameter, the magnetic and electric fields, confining frequency, and temperature. The results reveal that these parameters can tune the magnetic properties of the GaAs quantum dot and flip the sign of magnetic susceptibility from negative (diamagnetic) to positive (paramagnetic) type material.

Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran

PurposeThis research aims to introduce a new methodology for integration between urban design strategies and supervised machine learning (SML) method – by applying both energy engineering modeling (evaluating phase) for the existing green sidewalks and statistical energy modeling (predicting phase) for the new ones – to offer algorithms that help to catch the optimum morphology of green sidewalks, in case of high quality of the outdoor thermal comfort and less errors in results.Design/methodology/approachThe tools of the study are the way of processing by SML, predicting the future based on the past. Machine learning is benefited from Python advantages. The structure of the study consisted of two main parts, as the majority of the similar studies follow: engineering energy modeling and statistical energy modeling. According to the concept of the study, at first, from 2268 models, some are randomly selected, simulated and sensitively analyzed by ENVI-met. Furthermore, the Envi-met output as the quantity of thermal comfort – predicted mean vote (PMV) and weather items are inputs of Python. Then, the formed data set is processed by SML, to reach the final reliable predicted output.FindingsThe process of SML leads the study to find thermal comfort of current models and other similar sidewalks. The results are evaluated by both PMV mathematical model and SML error evaluation functions. The results confirm that the average of the occurred error is about 1%. Then the method of study is reliable to apply in the variety of similar fields. Finding of this study can be helpful in perspective of the sustainable architecture strategies in the buildings and urban scales, to determine, monitor and control energy-based behaviors (thermal comfort, heating, cooling, lighting and ventilation) in operational phase of the systems (existed elements in buildings, and constructions) and the planning and designing phase of the future built cases – all over their life spans.Research limitations/implicationsLimitations of the study are related to the study variables and alternatives that are notable impact on the findings. Furthermore, the most trustable input data will result in the more accuracy in output. Then modeling and simulation processes are most significant part of the research to reach the exact results in the final step.Practical implicationsFinding of the study can be helpful in urban design strategies. By finding outdoor thermal comfort that resulted from machine learning method, urban and landscape designers, policymakers and architects are able to estimate the features of their designs in air quality and urban health and can be sure in catching design goals in case of thermal comfort in urban atmosphere.Social implicationsBy 2030, cities are delved as living spaces for about three out of five people. As green infrastructures influence in moderating the cities’ climate, the relationship between green spaces and habitants’ thermal comfort is deduced. Although the strategies to outside thermal comfort improvement, by design methods and applicants, are not new subject to discuss, applying machines that may be common in predicting results can be called as a new insight in applying more effective design strategies and in urban environment’s comfort preparation. Then study’s footprint in social implications stems in learning from the previous projects and developing more efficient strategies to prepare cities as the more comfortable and healthy places to live, with the more efficient models and consuming money and time.Originality/valueThe study achievements are expected to be applied not only in Tehran but also in other climate zones as the pattern in more eco-city design strategies. Although some similar studies are done in different majors, the concept of study is new vision in urban studies.

TLmutation: Predicting the Effects of Mutations Using Transfer Learning.

A reccurring challenge in bioinformatics is predicting the phenotypic consequence of amino acid variation in proteins. With the recent advancements in sequencing techniques, sufficient genomic data has become available to train models that predict the evolutionary statistical energies, but there is still inadequate experimental data to directly predict functional effects. One approach to overcome this data scarcity is to apply transfer learning and train more models with available data sets. In this study, we propose a set of transfer learning algorithms we call TLmutation, which implements a supervised transfer learning algorithm that transfers knowledge from survival data of a protein to a particular function of that protein. This is followed by an unsupervised transfer learning algorithm that extends the knowledge to a homologous protein. We explore the application of our algorithms in three cases. First, we test the supervised transfer on 17 previously published deep mutagenesis data sets to complete and refine missing data points. We further investigate these data sets to identify which mutations build better predictors of variant functions. In the second case, we apply the algorithm to predict higher-order mutations solely from single point mutagenesis data. Finally, we perform the unsupervised transfer learning algorithm to predict mutational effects of homologous proteins from experimental data sets. These algorithms are generalized to transfer knowledge between Markov random field models. We show the benefit of our transfer learning algorithms to utilize informative deep mutational data and provide new insights into protein variant functions. As these algorithms are generalized to transfer knowledge between Markov random field models, we expect these algorithms to be applicable to other disciplines.

K X‐ray satellite structures of bromine and molybdenum

The energies and electric dipole rates of Kα X‐ray satellites due to 1s‐2p transitions in bromine and molybdenum with one to five vacancies in the 2p shell and other shells retaining their normal occupation numbers are computed using Multi‐Configuration Dirac Fock wavefunctions with the inclusion of relativistic and quantum electrodynamic corrections. As fully relativistic calculations on all the Kα X‐ray fine structure satellites with multiple spectator vacancies in the 2p shell of elements under consideration, to the best of our knowledge, are reported in this work for the first time, the accuracy of the E1 data is analyzed in terms of the agreement between the rates in the length and velocity forms. While the intensities of the strong electric dipole lines in the various groups of 1s ‐ 2p transitions obey the well established Z dependence, many of the weak transitions vary randomly with Z and an attempt is made in this work to understand the behaviour of the spin‐forbidden and weak electric dipole lines in the KαL n X‐ray satellite structure of Br, Mo, I, and Au. As the number of closely spaced fine structure transitions range from a few to many depending on the spectator vacancies in the 2p shell, the intensity weighed statistical average satellite energies, line strengths and photo‐ionization cross sections are reported to guide in the reliable interpretation of experimental measurements.

Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms

Bayesian analysis has attracted more attention in calibrating building energy models since it can naturally account for uncertainty of input parameters. However, standard Bayesian analysis requires to compute likelihood functions to represent the probability of the observed data under a statistical energy model. This makes the implementation of standard Bayesian analysis difficult for calibrating dynamic building energy models. Therefore, this research proposes a new Bayesian method by combining the approximate Bayesian computation with machine learning techniques in calibrating building models without computing likelihood functions. The results indicate that this new Bayesian computation approach combined with the machine learning algorithms can provide fast and reliable calibration for building energy models created using the EnergyPlus program. The calibration metrics in terms of the coefficient variations of root mean square errors from this new Bayesian method are well below the threshold value (15%) recommended from the ASHRAE standard for monthly energy data. Moreover, the better calibration results can be obtained by using linear or nonlinear post-adjustment techniques to account for the differences between the simulated and observed energy data. The method proposed here can significantly extend the application of Bayesian analysis in calibrating building energy models under uncertainty due to its simplicity and fast computation. Furthermore, the new Bayesian approach combined with machine learning technique can also be used to calibrate the simulation-based models for various types of energy systems.

Road Noise Evaluation by Sound Quality Simulation Module

<div class="section abstract"><div class="htmlview paragraph">An objective evaluation of sound quality is a technical bridge connecting sound pressure level (SPL) and human auditory sensation. In this paper, an algorithm is proposed for calculating objective evaluation parameters of sound quality (including loudness, sharpness and articulation index), considering acoustic characteristics of human external ear, middle ear and inner ear to reflect auditory sensation. A sound quality simulation (SQS) module is coded according to the algorithm. The module is used for evaluating sound quality of road noise from an SUV in three steps. Firstly, interior noise is predicted by integrating finite-element method (FEM), hybrid FE-SEA method, and statistical energy analysis (SEA) for low frequency (20~315 Hz), medium frequency (315~500 Hz), and high frequency (>500 Hz) in 1/3 octave band, respectively. The predicted interior noise SPLs are compared with the measured results, with deviations less than 3dB in average. Secondly, the sound quality parameters are calculated using the predicted SPLs in the SQS module. The predicted and measured loudness, sharpness, and articulation index are compared, with average deviations less than 5%. Finally, the predicted interior noise is filtered by the SQS module in 1/3 octave band, to determine the dominant contribution bands for the sound quality parameters. Several optimized designs are implemented to optimize the sound quality parameters, and validated by experiments.</div></div>

What a little branching can do – Dissociative photoionization of two butanol isomers

The fragmentation processes of two internal energy-selected C4H10O+ isomers, 1-butanol and isobutanol cations, were investigated by imaging photoelectron photoion coincidence (iPEPICO) spectroscopy. The first dissociation channel leads to the formation of C4H8+ ions (m/z 56) by water loss in both isomers. Using statistical energy distribution and rate models including the isomerization of the parent ion, the 0 K appearance energies (E0) were determined to be 10.347 ± 0.015 eV and 10.57 ± 0.05 eV for 1-butanol and isobutanol, respectively. The second dissociation channel, the formation of CH3OH2+, quickly overtakes the water-loss channel in isobutanol with an E0 of 10.61 ± 0.02 eV. It appears only as a minor channel in 1-butanol with an E0 of 10.74 ± 0.09 eV. The methanol-loss channel, forming propylene ion, opens up at E0 = 10.94 ± 0.04 eV and 10.72 ± 0.02 eV in 1-butanol and isobutanol, respectively. The next two fragmentation pathways correspond to complementary pair formation of C3H7(+/o) and CH2OH(o/+). The former is assigned as the isopropyl (ion) in both butanol isomers. The channel corresponds to simple bond cleavage in isobutanol with the E0 difference corresponding to the ionization energy difference of the fragments at E0 = 10.97 ± 0.05 eV (C3H7+) and at E0 = 11.11 ± 0.20 eV (CH2OH+). However, there is an internal hydrogen shift necessary in 1-butanol and, therefore, the complementary ions appear at the same E0 = 11.10 ± 0.03 eV, which corresponds to a shared rate limiting transition state. The sequential dissociation product from m/z 56, C3H5+, appears above 11.6 eV as a minor channel in both isomers. Finally, we determined −279.1 ± 1.6 kJ mol−1 as the 298 K heat of formation of gaseous isobutanol. We also propose revised ionization energies for the two butanols isomers based on composite method calculations.

Determination of SEA loss factors by Monte Carlo Filtering

Actual use of Statistical Energy Analysis for complex systems requires the numerical or experimental determination of the SEA loss factors (E-SEA). This approach involves experimental and numerical issues that can lead to results inconsistent with the SEA hypothesis, namely, the SEA loss factors obtained may be negative. This work presents an analysis of the E-SEA energy matrix class that shows that it is not well suited for this type of inverse problem. In addition, a method based on Monte Carlo Filtering capable of overcoming this inconsistency is presented, so that robust values of SEA loss factors can be obtained from experimental results. The method is described and applied to two application cases to show its practical implementation, the advantages that the method can offer and its comparison with other methods presented in the literature for the same purpose.

A similitude for the middle-frequency vibration response of satellite solar array based on the wave coupling hybrid finite element–statistical energy analysis method

A similitude is proposed for the dynamic response analysis of a satellite solar array. It is defined by invoking the wave coupling hybrid finite element–statistical energy analysis method which is considered as an effective way to solve the wide frequency (especially middle-frequency) vibration response of complex structures. The scaling laws are then derived by looking for equalities in the structural responses. A numerical case involving an assembly of two plates is discussed to verify the proposed scaling laws. Moreover, the new similitude is applied to a simplified satellite solar array model. If the analysis frequency step is selected appropriately, it is shown that a complete similitude can be defined: it allows predicting the vibration response of satellite solar array and provides the possibility for the model test of complex structures.

Extensive nature of long range interactions: role of disorder

The omnipresent disorder in physical systems makes it imperative to investigate its effect on the spatial range of interactions for which system remains thermodynamically extensive. Previously known bounds on the statistical free energy for clean systems [] indicate it to be extensive only for the spatially short range interactions (decaying faster than r−d at large distance r with d as system dimension). We analyze the bounds for quantum systems with different types of disordered many body potentials e.g. annealed, quenched, Gaussian or power law distributed. Our results indicate the dependence of the bounds on the multiple distribution parameters representing the potential which in turn permits, in contrast to clean potentials, more freedom to achieve the extensive limits even for arbitrary spatial ranges of the interactions.

Determination of Coupling Loss Factors Between Two Plates Joined at a Right Angle Using the Wave Approach

Abstract Vibration transmission through two homogenous isotropic plates, joined at a right angle to each other, is studied using a wave approach in the audible frequency range. In this study, a generalized mathematical model is developed for the right-angled joint using a spring and dashpot model, which represents welded, riveted, and bolted joints. The energy transfer for the spring-dashpot model is determined in terms of transmission and reflection efficiencies of the aforementioned joints. The effect of variation of stiffness and damping of the joints on the transmission and reflection efficiencies is studied for variation in the coupled plate thickness and density ratio. The transmission efficiency is used to theoretically determine the coupling loss factor (CLF), which is an important parameter of statistical energy analysis (SEA) models. It is observed that with an increase in the mass of the second plate, it acts as a blocking mass to energy transfer. Experiments were performed on two mild steel plates connected using weld, rivet, and bolt joints to determine the CLFs for the joints and to verify the theoretical model. The CLFs determined from experiments and predicted using the wave approach were compared with those determined from the power injection method (PIM).

Layout optimization of viscoelastic damping for noise control of mid-frequency vibro-acoustic systems

This paper presents an integrated, new, and generic framework for layout optimization of viscoelastic damping for noise control of mid-frequency vibro-acoustic systems. The method is developed based on the concept of power balance among different modal energies between coupled structural and acoustic subsystems and is formulated within the framework of a statistical modal energy distribution analysis (SmEdA). In the novel optimization formulation, the total energy of the acoustic subsystem is chosen as the objective function for minimizing the internal acoustic response in the vibro-acoustic system; and the relative material volume densities for viscoelastic element groups are selected as design variables using a volume-preserving Heaviside function. A new sensitivity analysis formulation is developed in a semi-analytical form via a SmEdA for solving the vibro-acoustic optimization problem. Two numerical examples are presented to demonstrate the efficiency and effectiveness of the present method. The present numerical results reveal two important findings: (a) the total acoustic energy of the chosen vibro-acoustic system can be significantly reduced; and (b) the optimum viscoelastic material layouts not only decrease the peak values of the modal coupling strengths between structural and acoustic subsystems but also create relatively more uniform acoustic modal energy distribution.

A review of the different approaches to predict the sound transmission loss of building partitions

The computation of sound transmission through solid structures has been of great interest since the first half of the 20th century. Several prediction methods can be found in the large body of literature, applicable to many kinds of structures, and derived using different approaches. This article presents a review of the most significant approaches to compute sound transmission through a building partition; it aims to provide an extensive update and critical literature review of the different approaches which can be used to compute sound transmission through a building partition. Different approaches, suitable for different kinds of building structures, based on analytical and semi-analytical solutions, wave-propagation, numerical or statistical energy analysis methods are described, highlighting advantages and drawbacks.

Prediction of the acoustic effect of an interior trim porous material inside a rigid-walled car air cavity model

In this paper, a simplified car air cavity model for predicting the influence of an interior trim material on the cabin interior sound pressure level (SPL) was presented. This work studies a rigid-walled car cabin model with one flexible wall. The interior trim material (a porous sound-absorbing material) was mounted in between the enclosed car air cavity and the flexible wall. A single point acoustic source is used to acoustically excite the rigid-walled car air cavity model, where the loudspeaker is positioned at front corner of the car cabin to simulate noise entering via an airborne path. The variation of the car air cavity interior sound pressure level corresponding to the presence of different porous sound-absorbing materials was experimentally investigated for high frequencies. The acoustical effects of the interior trim porous material on the proposed structural and acoustic coupled system were predicted by using the Statistical Energy Analysis (SEA) method for noise in one third octave bands. The comparison of results revealed that the experimentally acquired car cabin interior sound pressure level agrees fairly well with the simplified SEA model, both with and without the porous sound-absorbing materials, for high frequency one third octave band levels.

Improving the double-exponential windowing method to identify modal frequencies and damping ratios of dynamically large structures

Dynamically large structures can have many closely spaced modes. A particular example is an extruded aluminium panel used for building high-speed trains. Such a structure may be modelled deterministically with a large number of degrees of freedom (dofs), or statistically represented as a statistical energy analysis (SEA) system having particular SEA parameters. In both cases, experimental modal parameter identification may be required. Although there are several commercial packages which can be used for modal parameter identification, alternative methods with different strengths and/or convenience are still desirable. In this paper, the double-exponential windowing method developed by the second author is revisited and improved. Improvements are achieved by taking into account the effect of the finite time duration of Fourier transforms and making use of the real parts of driving point mobilities. The method is easy to implement and the improvements make the choice of the decay rate in the exponential window much flexible: for a very lightly damped structure, it allows to use a large positive decay rate to depress measurement noise, and for a highly damped structure, it allows to use a negative decay rate to make modes more evident or to separate overlapped modes. The usefulness of the improved method are demonstrated for a 30-bladed wheel model with known modal parameters, and for an extruded aluminium panel.

Neighborhood Preference of Amino Acids in Protein Structures and its Applications in Protein Structure Assessment

Amino acids form protein 3D structures in unique manners such that the folded structure is stable and functional under physiological conditions. Non-specific and non-covalent interactions between amino acids exhibit neighborhood preferences. Based on structural information from the protein data bank, a statistical energy function was derived to quantify amino acid neighborhood preferences. The neighborhood of one amino acid is defined by its contacting residues, and the energy function is determined by the neighboring residue types and relative positions. The neighborhood preference of amino acids was exploited to facilitate structural quality assessment, which was implemented in the neighborhood preference program NEPRE. The source codes are available via https://github.com/LiuLab-CSRC/NePre.

A novel hybrid deterministic-statistical approach for the mid-frequency vibro-acoustic problems

It is difficult to predict precisely the frequency response of a complex vibro-acoustic system in mid-frequency region. To overcome this deficiency, a novel hybrid stable node-based smoothed finite element method/statistical energy analysis model is proposed in this work. The whole vibro-acoustic system can be divided into a combination of a structural subsystem with statistical behavior and an acoustic subsystem with deterministic feature. The recently developed stable node-based smoothed finite element method is employed here to simulate the deterministic subsystem, and the statistical energy analysis is utilized to deal with the statistical subsystem. Based on the so-called diffuse field reciprocity relation, these two subsystems can be easily connected and coupled. Due to the introducing of gradient smoothing and compensation operation, our algorithm can significantly reduce the dispersion error compared with the traditional finite element method. Thus, it is expected that the present coupled model can provide ultra-accurate results. Numerical examples, including both benchmark and practical engineering cases, demonstrate that our algorithm works very well in mid-frequency vibro-acoustic analysis.