Statistical Energy Research Articles

Acoustic performance prediction by means of a statistical energy analysis of two adjacent workrooms used for conferences and educational proposes

AbstractThe current design of workspaces for companies is focused on minimalistic and modern styles prioritizing the aesthetic appearance with illuminated open spaces and the use of glass walls. If the acoustic performance is not considered, inappropriate Reverberation Times (RT) might lead to difficulties for hearing a speaker or teacher during courses and conferences, causing problems in the learning process. Additionally, there might be high sound transmission between adjacent work rooms. This paper presents the development and experimental validation of numerical models using Statistical Energy Analysis (SEA) to calculate the sound reduction index through a glass wall that separates two adjacent work rooms. These structures are dedicated to conferences and educational uses but cannot be properly used due to their high sound transmission and inappropriate reverberant time. The sound insulation prediction results are validated with experimental measurements carried out in the adjacent rooms under the standard ISO-140. Afterwards, a SEA model is used to provide some acoustic correction design guidelines for these types of constructions. To improve the acoustical performance in the rooms, the acoustic effects of different absorbent panels placed on the walls and ceiling were investigated, choosing a suitable material that complies with the recommended ranges of RT and with less amount of absorption area. The SEA model is then used to understand the effects of openings size between the panels that make up the glass wall on the sound insulation capacity between rooms. Finally, a SEA model is reformulated to quantify the effect of the application of double walls for sound insulation between rooms, which implies the increase of weighted sound reduction index in 9 dB with respect to measured data.

Wind power plant site selection problem solution using GIS and resource assessment and analysis of wind energy potential by estimating Weibull distribution function for sustainable energy production: The case of Bitlis/Turkey

Wind energy, being a sustainable energy source, is a benign, dependable, limitless, and cost-effective source of energy. This study involved the creation of a wind power plant site selection map at a provincial scale using a GIS application. The map took into account both environmental risks and environmental criteria. A wind energy measurement station of the PCE-FWS 20 type, equipped with a touch screen, was installed in the Rahva campus of Bitlis Eren University. Over the course of one year, data on wind direction, wind speed, temperature, relative humidity, and air pressure were collected at 5-s intervals and averaged over 20-min periods. The purpose of this study was to assess the suitability of the measured region for wind energy potential. A comprehensive statistical analysis was conducted using all the data collected during this period to assess the wind energy potential in the studied region. The Weibull distribution was employed to analyse the wind energy potential of this particular region. We employed graphical techniques to ascertain the Weibull distribution. The wind data analysis and simulation were conducted using the Matlab application. Analysed were the monthly statistical wind energy potential of the region, wherein the average wind speed and power density values were determined and presented through graphs at 20-min intervals. Upon analyzing the map generated using the Geographical Information Systems (GIS) technique, it has been ascertained that Bitlis Eren University Rahva campus is located in favorable areas.

A Statistical, Photometric, and SED Analysis to Characterize the BSS Populations in Old Open Cluster: Berkeley 39

ABSTRACT We present a statistical, photometric, and spectral energy distribution (SED) analysis to characterize the blue straggler stars (BSSs) populations in the Galactic old open cluster Berkeley 39. Berkeley 39 is a 6.16 Gyr old open cluster located at a distance of 3.99 Kpc.Gaia DR3 astrometry data have been used to estimate the membership probabilities using ensemble-based unsupervised machine learning techniques. We identified 21 BSS candidates on the colour–magnitude diagram, with 19 of them being detected in the Swift/UVOT UVW2 filter. We analysed the radial surface density profile and examined the cluster dynamical states and mass segregation effect. The SEDs of 19 BSSs are constructed using multiwavelength data covering UV to IR wavelengths. A single-component SED is fitted successfully for 14 BSS candidates. We discovered hot companions in five BSS candidates. These hot companions have temperatures of approximately 14 000 to 23 000 K, radii ranging from 0.04 to 0.13 R$_{\odot }$, and luminosities ranging from 0.16 to 2.91 L$_{\odot }$. Among these, three are most likely extremely low-mass white dwarfs (WDs) with masses around 0.17 to 0.18 M$_{\odot }$, and two are low-mass WDs with masses around 0.18 to 0.39 M$_{\odot }$. This confirms that they are post-mass transfer (Case A or Case B) systems. We also investigated the variable characteristics of BSSs by analysing their light curves using data from TESS. Our analysis confirms that two BSSs identified as eclipsing binaries in Gaia DR3 are indeed eclipsing binaries. Additionally, one of the two eclipsing binary BSSs shows evidence of having hot companions, as indicated by the multiwavelength SEDs.

Coupling model between building post structure and floors with the spectral element method

With the densification of urban area and public transport, the structure-borne noise disturbance due to railway traffic is a major issue. In order to mitigate it, ground-borne vibration propagation in building has to be investigated. The case of post-beam constructions necessitates 3D modeling with choices between complex, precise and computationally time-consuming approaches (3D finite element method for example), and fast but less accurate approaches like the statistical energy analysis (SEA) which need a strong modal density. For such post-beam constructions, the spectral element method (SEM) appears to be a good compromise. This method allows reducing the number of elements to represent the truss structure compared to the finite element method. However, the building walls and floor are difficult to consider with the SEM. In this paper, the challenge of coupling post-beam structures and floors is examined, a method is proposed and results are discussed. The aim is to develop a method applicable to different buildings while keeping advantage of SEM low discretization.

Large commercial business aircraft acoustic design with SEA

As air travel becomes more frequent, customers are demanding a quieter cabin environment. Over the past two decades, Gulfstream has developed a suite of predictive tools based on Statistical Energy Analysis (SEA) to create a quiet cabin on business aircraft. This paper discusses the most recent collaboration between Gulfstream and Jet Aviation to develop an acoustic design for a large commercial business aircraft. SEA analysis played a key role to identify the risks, determine the driving mechanisms, and develop solutions to meet the established target. Besides SEA model development and analysis, this paper will also discuss some unique challenges to this aircraft and some innovations that were developed to mitigate them. One year after the acoustic design was released, flight test data on the completed aircraft confirmed the success of the acoustic design as the quietest cabin to date on the specific aircraft type.

Wooden multistorey acoustic test facility for vibration reduction indices measurement on different junctions of floor decking used to reduce flanking transmissions

FCBA, CSTB and Cerqual joint forces together with the French acoustic wood technical commission, to create in 2020, an in-situ measurement mockup: the "Maquette Acoustique Bois". The mockup is a CLT based building of three floors construction, with four rooms on each level. Measurements from junction characterization to air-borne and impact sound insulations can be performed. In this paper the "Maquette Acoustique Bois" is used to measure vibration reductions levels indexes Kij, in different configurations, where smart building systems junctions are implemented to reduce flanking transmissions. Measurement results presented are generated from 4 types of junctions in between 2 rooms. The load bearing structure is Cross Laminated Timber (CLT). The measurement strategy is Statistical Energy Analysis (SEA). SEA involves cutting the structure into subsystems and decomposing the spectrum into third-octaves or octaves. In this way, the exchange of energy flow in the substructures can be analyzed. The parameters that govern vibrational transmission between subsystems are damping and coupling loss factors and can be identified experimentally by reversing the problem. The approach is, first, based on sub-structuring strategy of structure; second, on experimentally measured coupling and damping loss factors; third, on extracting vibration level difference one by one all flanking passes.

An innovative approach for enclosure design of power generators to meet noise regulation targets

The use of diesel-powered engine generators as a power source is very common for various industrial and residential applications. There are strict regulatory requirements and legislations to limit the noise levels from these generators below statutory limits. The purpose of the paper is to present an approach using statistical energy analysis (SEA) augmented by experimental inputs for the prediction of noise levels for the generators and propose strategies for optimized enclosure design that effectively mitigate noise to achieve regulatory requirements. The study begins by developing an SEA model that represents the acoustic behavior of the generator and enclosure. The airborne sources such as engine, alternator, radiator fan and exhaust are modelled explicitly using experimental noise source characterization. Based on the established SEA model and excitation sources, noise levels are predicted through the calculation of energy flow between subsystems. The predicted sound levels for an existing Diesel Generator with enclosure were validated with its test data to ensure correctness of SEA model. Parametric studies were carried out using simulations on key variables, including critical noise paths, panel thickness, and noise control treatments. These investigations helped identify optimal process parameters that reduce noise levels emitted by genset in adherence to noise regulations.

Using wave based SEA methods to model noise and vibration in underwater structures across a broad frequency range

Modelling noise and vibration transmission in underwater structures is often challenging due to the large size of the structures and the need for analysis across a broad frequency range. Finite Element and Boundary Element methods are useful at lower frequencies but are typically not computationally tractable for system level models at mid and high frequencies. Statistical Energy Analysis (SEA) methods are widely used for modeling structures in air at mid and high frequencies. However, traditional SEA methods often do not contain enough detail to describe wave propagation in heavy fluid loaded structures in underwater applications. This paper discusses the use of a more general 'wave based' SEA method that uses periodic structure theory to accurately calculate the wavetypes of typical underwater structures. The paper illustrates the method by initially considering simple structural sections and showing the effects of: curvature, heavy fluid-loading, panel stiffeners/ribs. The method is then applied to a simplified full scale submarine model and comparisons are made with a nodal finite element model. The domain of validity and the hypotheses of the method are then discussed.

Innovative Strategies for designing acoustic encapsulations for e-motors to tackle NVH performance challenges

The automotive industry's rapid transition towards E-mobility is presenting novel challenges for NVH engineers having broadband frequency composition and tonal characteristics. The purpose of the paper is to present innovative strategies for electric motor NVH development by directly applying acoustic encapsulation made of poro-elastic materials onto the casing of the electric motor. The first strategy is a pure simulation approach with combination of finite element analysis (FEA) and statistical energy analysis (SEA). Finite Element Analysis is used to calculate radiated power of electric motor casing for Maxwell electromagnetic excitations on stator teeth. This radiated power is then applied as excitation on the SEA model of the electric motor casing for prediction of exterior noise using Statistical Energy Analysis. The second strategy is a hybrid approach of simulations and experimental measurements. Electric motor is characterized as a source in terms sound power from experimental measurements of sound pressure as per ISO Standard: ISO 3745:2003. This power is applied as excitation in the interior of SEA model of the electric motor for prediction of exterior noise. Both the strategies allow design of optimized sound package on the SEA model of electric motor using statistical energy analysis to achieve the NVH performance.

LCV Vehicle in-cab noise prediction and reduction using SEA based simulation approach

With increase in consumer demand vehicle OEMs are developing quieter vehicles at a design stage using acoustic simulation tools to reduce development cycle times avoiding costly proto builds and test iterations. Present work describes application of statistical energy analysis (SEA) simulation approach for in cab noise prediction and noise reduction through sound package optimization for LCV vehicle. In first step complete vehicle in-cab SEA model is prepared with baseline sound package concept. It consists of comprehensive definition of structural panels, interior and exterior cavities. Noise control treatment is modelled as multi-layer poro-elastic materials. Airborne noise excitations for sources engine, transmission, intake and exhaust muffler are applied. Source strengths for above sources were derived from dyno-coupled powertrain noise tests in the anechoic chamber. Load case of rated speed was considered neglecting tire and wind noise during simulation. Contribution analysis was carried out to find out sensitivity of different panels on interior noise at various frequencies. It was observed that bottom middle floor panel and rear glass panel were major noise contributors. Noise mitigation was carried out by optimizing floor panel sound package and intake air filter within given space constraints. These modifications resulted in overall 2 dB in-cab noise reduction

Simulating noise and vibrations on passenger ships using Statistical Energy Analysis

Various sources aboard a passenger ship contribute significantly to noise and vibration concerns. Notably, fluctuating pressure loads on the hull can result in elevated noise levels within rooms near the propulsion. Expensive solutions of sound insulation such as floating floors are employed to reduce these levels. Nowadays, designing these treatments is mainly based on empirical studies. However, the limited availability of the ship, the laborious nature of experiments, and the possibility of solutions being oversized have only increased the method's constraints. Utilizing calculation methods can save time, reduce additional weight on the ship, and provide more tailored design solutions. A Statistical Energy Analysis (SEA) has been used as deterministic approaches are often not suitable for the frequency range of interest for full-scale structures. The validation process for the wave-based SEA method involves several steps. Initially, simple models are constructed to establish correlations between local airborne and structure-borne energy transmission using simple acoustic loads, as well as to assess the method's scope of application. Subsequently, this approach is applied to larger models, such as the aft full part of the ship, enabling the prediction of noise levels and energy transmission paths under real loads.

Weight minimization of train floors with acoustic and structural constraints

For high-speed trains, high noise levels are generated in the bogie from wheel-rail contact and aero-acoustic sources. Manufacturers try to minimize the floor transmission to the interior, e.g. by applying damping layers or by other measures. For vehicle noise control measures, the goal is often to achieve the sound reduction needed at lowest possible mass. Here, an optimization scheme is applied to minimize the mass of an extruded floor panel. Constraints are defined by limiting the maximum local floor deflection from passenger loads according to EN 12663-1 and the A-weighted interior sound level. Recognizing the periodic nature of the floor structure, a Finite Element model of a single periodic cell is set up, using periodic boundary conditions and the floor sound reduction is determined using a Statistical Energy Analysis approach. An analytic beam model is applied to determine the floor deflection from the passenger load. Design solutions meeting the constraints with the smallest possible mass are sought. Starting with a floor structure with 23.3 kg/m2, a final design with 18.0 kg/m2 is reached. In addition, the transmitted sound through the floor is reduced by 8 dBA. Convergence from different starting points is verified.

Artificial neural network-based sound insulation optimization design of composite floor of high-speed train

Increasing the speed of high-speed trains requires the lightweight design of vehicles to meet the economic and ecological efficiency requirements of such trains. However, these objectives conflict with the interior noise control in high-speed trains because the sound insulation of panel structures follows the mass law principle. The train floor, the main train body structure of the high-speed train, is vital for interior noise control because its sound insulation performance directly affects the interior noise levels. Owing to the complexity of the composite floor system, reliable measurement and accurate estimation of its sound insulation performance are often time-consuming and laborious. To address this situation, this study proposes an artificial neural network (ANN)-based model to predict the sound insulation characteristics of a composite floor. First, a sound insulation model of the composite floor is built based on statistical energy analysis (SEA). The sound insulation performance of 200 cases of composite floors is calculated by varying the dimensions of the extruded floor, thickness of the webs, sound-absorbing material, and wooden floor to formulate a sound insulation database of composite floors. Next, an ANN model is introduced and trained on the sound insulation database. The sound insulation prediction results obtained using the ANN model are compared to the prediction results obtained using the experiment to validate its effectiveness. Subsequently, the NSGA-II optimization method is used to optimize the sound insulation structure of the composite floor. Compared with the regular composite floor structure, the optimized structure reduced the mass of the composite floor by 10.93 kg and increased the weight of the sound insulation ( Rw) by 6.3 dB. The proposed method can be an effective, economical, and efficient tool for vehicle designers and can help promote the sound insulation optimization design of high-speed train composite floors.

Control Mechanism and Noise Reduction Effect of CVDM on Rail Transit Steel Bridge: Experimental and Numerical Study

Steel bridge, as one of the main structural types in bridge design, has been widely used in rail transit. However, the train-induced vibro-acoustic problem of the bridge has become increasingly prominent. The vibration and noise characteristics must be more significant and complex than steel truss bridge because of too many components. Viscoelastic damping materials (VDMs) have the characteristics of both viscous liquid and elastic solid, and have better damping performance than other damping materials. Therefore, VDM plays an important role in vibration and noise reduction technology. In applications, VDM is usually combined with constraining materials to form composite viscoelastic damping materials (CVDM). In this paper, based on the dynamic receptance method and superposition principle, the coupling model of vehicle–track–bridge in the frequency domain is first established, and then the vibration and noise prediction model of a steel truss bridge with CVDM is established based on RUK theory and statistical energy analysis (SEA) method. Based on the prediction model, the natural dynamic characteristics of typical components of steel truss bridges with CVDM were investigated, and the control mechanism and influence rule of CVDM on train-induced vibration and noise of steel bridges were revealed.

Study on Vibration and Noise of Railway Steel–Concrete Composite Box Girder Bridge Considering Vehicle–Bridge Coupling Effect

A steel–concrete composite beam bridge fully exploits the mechanical advantages of the concrete structure and steel structure, and has the advantages of a fast construction speed and large stiffness. It is of certain research value to explore the application of this bridge type in the field of railway bridges. However, with the rapid development of domestic high-speed railway construction, the problem of vibration and noise radiation of high-speed railway bridges caused by train loads is becoming more and more serious. A steel–concrete composite beam bridge combines the tensile characteristics of steel and the compressive characteristics of concrete perfectly. At the same time, it also has the characteristics of a steel bridge and concrete bridge in terms of vibration and noise radiation. This feature makes the study of the vibration and noise of the bridge type more complicated. Therefore, in this paper, the characteristics of vibration and noise radiation of a high-speed railway steel–concrete composite box girder bridge are studied in detail from two aspects: the theoretical basis and a numerical simulation. The main results obtained are as follows: Relying on the idea of vehicle–rail–bridge coupling dynamics, a structural dynamics analysis model of a steel–concrete combined girder bridge for a high-speed railroad was established, and numerical program simulation of the vibration of the vehicle–rail–bridge coupling system was carried out based on the parametric design language of ANSYS 18.0 and the language of MATLAB R2021a, and the structural vibration results were analyzed in both the time domain and frequency domain. By using different time-step loading for the vehicle–rail–bridge coupling vibration analysis, the computational efficiency can be effectively improved under the condition of guaranteeing the accuracy of the result analysis within 100 Hz. Based on the power flow equilibrium equation, a statistical energy method of calculating the high-frequency noise radiation is theoretically derived. Based on the theoretical basis of the statistical energy method, the high-frequency noise in the structure is numerically simulated in the VAONE 2021 software, and the average contribution of the concrete roof plate to the three acoustic field points constructed in this paper is as high as 50%, which is of great significance in the study of noise reduction in steel–concrete composite girders.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches – tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory folded conformation, due to intrinsic sequence effects. Here we investigate the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a thermodynamic cycle involving many (n = 108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation DFG-out Activation Loop Folded, is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Bose–Einstein distribution temperature features of quasiparticles around magnetopolaron in Gaussian quantum wells of alkali halogen ions

Abstract We have applied strong coupling unitary transformation method combined with Bose–Einstein statistical law to investigate magnetopolaron energy level temperature effects in halogen ion crystal quantum wells. The obtained results showed that under magnetic field effect, magnetopolaron quasiparticle was formed through the interaction of electrons and surrounding phonons. At the same time, magnetopolaron was influenced by phonon temperature statistical law and important energy level shifts down and binding energy increases. This revealed that lattice temperature and magnetic field could easily affect magnetopolaron and the above results could play key roles in exploring thermoelectric conversion and conductivity of crystal materials.

Calculation of the coupling loss factors for the angle connected beams

The use of the statistical energy method for the analysis of dynamic systems assumes knowing coupling loss factors of the subsystems. Coupling loss factors show how much energy transfers from one subsystem to another. They are included in the system of power-balance equations and must first be determined analytically, experimentally or numerically. The most promising of the listed methods is numerical. In particular, this article uses the finite element method. The purpose of this study is to determine the coupling loss factors of two subsystems in two versions of their relative position. The basis is the model of an L-shaped connection of two beams, which is quite common in such studies. L-shaped connections of structural parts are prevalent in building structures, but in other areas, such as the development of space and aviation technology, structural elements with an angle other than 90° between them. Since energy methods can also be applied to the aerospace industry, when developing approaches to structural analysis using such methods, it will be useful to know how the energy system parameters, in particular the coupling loss factors, depend on the coupled angle. The paper considered two configurations of the system: in the first, the beams connected at a right angle, in the second, at an angle of 45°. We calculate the coupling loss factors of the beams for both system configurations. Conclusions of this paper draw about the possibility of extending the result to more complex structures, namely the spacecraft structures.

Determination of diffuse field sound absorption of a porous absorber from Biot parameters using simulation

Porous sound absorbers are commonly assessed by their diffuse field sound absorption. In the test, a sample of the material, having a recommended surface area of 2.44 m by 2.74 m, is placed on the floor of a reverberation room. The average sound pressure level in the room is measured using a reference sound source and compared to the level in an empty room. Using room acoustic theory, the diffuse field sound absorption of the sample can be determined. This sound absorption is often much higher than the normal incidence sound absorption due to diffraction effects especially at lower frequencies. In this research, a hybrid simulation method is used to determine the diffuse field sound absorption from the Biot properties of the sound absorber. Statistical energy analysis is used to model the room and finite element analysis is used to simulate the material sample. Results from simulation are compared against measurement and agreement is acceptable.

Numerical modeling of axial fan noise in fuel-cell electric truck: from component to full-vehicle analysis

This paper presents a comprehensive approach to predict the noise generated by an axial fan in electric vehicles, which is more noticeable due to the absence of combustion engines. Such cooling fans require stronger cooling performance due to increased demand for heat dissipation, resulting in even higher noise levels. A numerical model for fan noise in a fuel-cell electric vehicle (FCEV) is proposed with two steps: component-level and full-vehicle level models. At component level, Lattice Boltzmann Method (LBM) is employed for aeroacoustics simulation, determining the component sound power level (SWL). At full vehicle level, statistical energy analysis (SEA) is utilized to estimate the sound pressure level (SPL) inside the vehicle cabin, particularly at the driver's ear locations. The noise sources in the full vehicle model, were characterized from the component level SWL, as monopole sources. The model is validated by comparing the numerical predictions with the experimental measurements of the SWL in a hemi-anechoic chamber and the SPL in the cabin. Good agreements were obtained between numerical results and measurement results, demonstrating the effectiveness of the model. This allows for faster and more accurate virtual NVH design validations on large and complex systems where prototype measurements are difficult to conduct.