Statistical Energy Research Articles

Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan

Building energy modeling plays an important role in analyzing the energy efficiency of the existing building stock, helping in enhancing it by testing possible retrofit scenarios. This work presents an urban scale and place-based approach that utilizes energy performance certificates to develop a statistical energy model. The objective is to describe the energy modeling methodology for evaluating the energy performance of residential buildings in Milan; in addition, a comprehensive reference dataset for input data from available open databases in Italy is provided—a critical step in assessing energy consumption and production at territorial scale. The study employs open-source software QGIS 3.28.8 to model and calculate various energy-related variables for the prediction of space heating, domestic hot water consumptions, and potential solar production. By analyzing demand/supply profiles, the research aims to increase energy self-consumption and self-sufficiency in the urban context using solar technologies. The presented methodology is validated by comparing simulation results with measured data, achieving a Mean Absolute Percentage Error (MAPE) of 5.2%, which is acceptable, especially considering city-scale modeling. The analysis sheds light on key parameters affecting building energy consumption/production, such as type of user, volume, surface-to-volume ratio, construction period, systems’ efficiency, solar exposition and roof area. Additionally, this assessment attempts to evaluate the spatial distribution of energy-use and production within urban environments, contributing to the planning and realization of smart cities.

State-of-art of Liquid Hydrogen Carriers: Trends in the Selection of Organic Molecules

Abstract: Nowadays, fossil fuels represent the main energy source. According to the BP Statistical Review of World Energy report, in 2021, global energy consumption amounted to 595.15 EJ of which 82% was generated from natural gas, oil and coal. The energy consumption growth, rapid depletion of fossil fuels and increasing pressure on the environment threaten the continued sustainability of the global energy system. In this context, renewable energy sources (RES), which now account for 6.7% are attracting increasing attention. The key obstacles to the introduction of RES (solar, wind geothermal, etc.) are their nonstationarity due to seasonality, meteorology and differences in geoclimatic conditions. In this regard, an important role is played by the development of technologies for efficient storage and transportation of renewable energy to consumers. One of the most promising storage technologies is the processing of renewable energy into hydrogen, which, due to the high mass energy intensity (120 MJ⋅kg-1) and environmental friendliness, can be considered a promising energy carrier. Nevertheless, the widespread use of hydrogen as a fuel is limited due to the low volumetric energy density and high explosiveness. Thus, along with the development of technologies for processing renewable energy sources into hydrogen (e.g., electrolysis), a large number of studies are focused on the development of technologies for storage and transportation. This study provides a brief overview of the state of the art of these technologies, with a focus on technology based on the use of liquid organic hydrogen carriers (LOHCs).

Estimation of input power to track energy propagation through a small air handling unit

Statistical energy analysis (SEA) is dependent on having accurate inputs. The inputs to an SEA model are structural and acoustic input powers. In this work, blocked force analysis is utilized to determine the structural and acoustic input powers to a small air handling unit (AHU). An electromagnetic shaker connected to a small stiff plate bolted to a panel of the AHU is used as an input force to the unit. The acoustic input consists of a calibrated volume velocity source placed inside the air handler. The inversely determined input powers are then used as inputs to the SEA model to track the structure-borne and airborne energy propagation through the small air handler. The results demonstrate that the inversely determined input powers can be used to reliably estimate inputs for the SEA model.

Global Temperature Projections from a Statistical Energy Balance Model Using Multiple Sources of Historical Data

Abstract This paper estimates a two-component energy balance model as a linear state-space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of nonstationarity in forcing and the incorporation of multiple data sources for the unobserved processes. We estimate the EBM-SS model using historical datasets at the global level for the period 1955–2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under representative concentration pathway scenarios are comparable to those from Coupled Model Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth system models. Significance Statement We develop a statistical model to understand Earth’s energy balance and how it impacts temperature across the planet’s surface and deep oceans. Unlike previous approaches, ours is fully statistical. This means that we use maximum likelihood to calculate the estimates and evaluate the level of uncertainty in the model’s parameters. By applying the model to historical data from 1955 to 2020, we are able to make predictions about the average global surface temperature that are consistent with those from the Coupled Model Intercomparison Project. Our findings support current predictions about global temperatures using a straightforward statistical climate model, grounded in historical data.

Comparison of FEA and SEA methods for predicting railway-induced vibration propagation through over-station developments

The design process for Over-Station Developments (OSDs) includes consideration of the potential impacts of railway-induced vibrations to ensure that structure-borne noise and occupant comfort standards are maintained during railway operations. Should an OSD be identified as sensitive to railway operational structure-borne noise or vibration impacts, the inclusion of adequate track vibration isolation measures becomes essential for mitigation. This paper presents a comparative study of two methodologies for predicting vibration propagation through structures and consequent structure-borne noise levels—Finite Element Analysis (FEA) and Statistical Energy Analysis (SEA). Specifically, this study provides an indication of the range of predicted results and emphasizes the computational efficiency and scalability of these methods when determining track isolation requirements for structures.

Limitations of the scattering coefficient for simulating early reflections

The introduction of the scattering coefficient has enabled geometrically-based acoustic simulations to produce full-length impulse responses of fairly high accuracy. However, the scattering coefficient is a random-incidence quantity that can be applied only when considering a statistical energy average over different directions. This model is inaccurate if a single reflection by a surface element is considered for which the scattered part of the sound energy depends strongly on the angle of incidence. The resulting deviation can be particularly relevant for first-order reflections as these reflections have significant influence on acoustic parameters that make use of early-energy estimates, such as D, C80 or LF. In this paper, the uncertainty of the specularly reflected energy is estimated and discussed for a number of typical surface structures and scenarios. The corresponding impact on the accuracy of predicted early-energy parameters is also discussed. Possible solutions and extensions such as an angle-dependent scattering model are outlined.

A review of structure-borne noise from elevated rail structures

In the general case, noise from elevated rail systems consists of direct and reverberant airborne noise radiated from wheels and rails, noise from on-train sources, aerodynamic noise, and structure-borne noise reradiated from the viaduct or bridge structure. This paper presents a method of predicting structure-borne noise radiating from viaducts due to rail operations. The structure-borne noise is calculated by first determining the dynamic force input to the elevated structure at the rail attachment points due to excitation from wheel-rail roughness. Vibration transfer between structural elements, such as parapets and structural webs is estimated using Statistical Energy Analysis (SEA), which is also used to calculate the sound power radiating from the structure. The prediction of receiver sound pressure levels is carried out using simple noise propagation models that account for the geometric relationships between structural elements and receiver locations.

Investigation of statistical energy analysis coupling loss factors of vibro-acoustic systems under thermal environment

Vibro-acoustic systems in aviation engineering could operate in a thermal environment and suffer from high-frequency dynamic excitations. The high-frequency vibro-acoustic problems are commonly solved by the Statistical Energy Analysis method, in which the Coupling Loss Factor is the key parameter for modeling vibro-acoustic systems. This work investigates the CLFs of vibro-acoustic systems under thermal environment. First, based on the modal data considering different thermal effects, the modal CLFs between structural modes and acoustic modes are predicted by the Statistical modal Energy distribution Analysis. Then, the CLFs between the structural subsystem and the acoustic subsystem are calculated. The simulation models employed in this research include the coupled plate-cavity system and the cylindrical shell-cavity system. The influence of thermal effects on the CLFs is discussed. For the investigated cases, in which the boundaries of structural susbsystem are set to simply supported, the results demonstrate that the CLFs decrease with increasing temperature when only the temperature-dependent material properties are considered. However, when only the additional stress stiffness is considered, the CLFs experience a more dramatic decrease compared to when only the temperature-dependent material properties are taken into account. Moreover, when both effects are considered, the influence of the additional stress stiffness has a leading effect on the CLFs. The proposed method can be conveniently applied to various vibro-acoustic systems operating in the thermal environment.

Comparison of Clustering-Based Virtual SEA Subsystem Generation Models

Building a valid Statistical Energy Analysis (SEA) model is far from straightforward in real life applications because it has to comply with certain requirements. Since intuition-based SEA subsystems do not always entirely meet these, more advanced methods such as clustering techniques have been proposed for model building. This paper focuses on the comparison of different clustering techniques from SEA model validity point of view in the case of a simplified car body model. Besides the compliance with the SEA theory, each obtained model is compared to a reference finite element simulation to assess its accuracy.

RNA tertiary structure modeling with BRiQ potential in CASP15.

We describe the modeling method for RNA tertiary structures employed by team AIchemy_RNA2 in the 15th Critical Assessment of Structure Prediction (CASP15). The method consists of the following steps. Firstly, secondary structure information was derived from various manually-verified sources. With this information, the full length RNA was fragmented into structural modules. The structures of each module were predicted and then assembled into the full structure. To reduce the searching conformational space, an RNA structure was organized into an optimal base folding tree. And to further improve the sampling efficiency, the energy surface was smoothed at high temperatures during the Monte Carlo sampling to make it easier to move across the energy barrier. The statistical potential energy function BRiQ was employed during Monte Carlo energy optimization.

Exploring Breast Cancer Texture Analysis through Multilayer Neural Networks

Breast cancer is a significant health problem for women globally; however, timely detection can reduce female morbidity and mortality. Early breast screening has become imperative for all women, though, adequate screening facilities are necessarily required in developing countries like Pakistan, where breast cancer is a leading cause of death. To encounter this chronic disease, various image processing techniques have been introduced to automatically diagnose breast cancer from digital mammograms. The current study deployed data from a population of 35 participants. The mammograms used for screening were 5 normal, 15 benign, and 15 malignant patients. The breast images were marked by the radiologist and the system was trained with normal, benign, and malignant classes. Moreover, Multilayer Neural Networks (MNN) based texture analysis methodology was adopted to distinguish normal, benign, and malignant breast images. Reportedly, an automated approach was used to detect breast conditions after conducting the analysis of digital mammograms. Statistical parameters, namely sum, mean, variance, standard deviation, kurtosis, skewness, energy, and entropy were calculated, analyzed, and compared for the normal, malignant, and benign breast images. The results indicated a 100% accuracy after the analysis. The results of the extracted statistical parameters were promising and reliable in distinguishing between normal, malignant, and benign breast mammograms, again indicating the need for early detection of the disease to minimize the risk of breast cancer among women.

Statistical singularity energy in ferroelectric phase transitions

Statistical singularity energy in ferroelectric phase transitions

Investigation of sound transmission through a finite length cylindrical shell in the presence of an exterior turbulent boundary layer

This paper studies sound transmission through a thin cylindrical shell of finite length. The structure is subjected to an exterior turbulent boundary layer (TBL) excitation and the boundary conditions of its two ends are considered as simply-supported. The classical shell theory (CST) is employed to derive wave propagation in the cylindrical shell, and the Corcos model is applied to describe pressure fluctuation due to TBL. According to the existence of two axial and radial modes, a complete convergence study is presented. To examine sound transmission through the structure, a comprehensive parameters study is provided on the resulting power spectral density (PSD) of the kinetic energy. To validate the results of the present method, the statistical energy analysis (SEA) method is used. For this purpose, modeling is performed by SEA in VA One software, which shows the high accuracy of the results of the present method. The analytical predictions suggest that the sound insulation performance of such a finite length structure is enhanced considerably by increasing the thickness of the shell in a broad-band frequency range. Similar effects on sound transmission are observed for Young’s modulus and density of the shell particularly in the low and high-frequency ranges, respectively. Whereas, enhancing the turbulent pressure and freestream velocity adversely affects sound radiation into the structure.

Mid Frequency Structure-Borne Noise Modeling in a Trimmed Automotive Vehicle

Finite Element Method (FEM) and the Statistical Energy Analysis (SEA) are standard methods in the automotive industry for the prediction of vibrational and acoustical response of vehicles. The FE method, based on a deterministic description of the dynamics is typically used for structure-borne and air-borne problems and is usually limited to below 250 Hz. The method becomes prohibitively expensive in terms of computational costs with increasing frequency as more Degrees-of-Freedom (DOF) are required to correctly represent the dynamic response of the structure. Additionally, the dynamic response becomes sensitive to small perturbations in the model, thus defeating the purpose of a deterministic modeling method. SEA is a statistical method widely applied in automotive industry for air-borne problems at frequencies higher than 300 Hz. The method is highly efficient for the design and optimization of sound packages facing airborne sources, but it lacks accuracy when dealing with structure-borne sources in the mid frequency range. However, both methods are not capable of handling the so called "mid frequency problem", where both short and long wavelength components are present in the same system. A Hybrid method has been recently proposed that rigorously couples SEA and FEM. In this work, the HybridFE-SEA method has been used to create fast/efficient model of structure-borne noise in a fully trimmed vehicle from 200Hz to 1 kHz. This paper first describes simple guidelines used to partition the vehicle into stiff components described with FE and modally dense components described with SEA. It is also demonstrated how detailed local FE models can be used to improve SEA descriptions of car panels and couplings. After review of the Hybrid FE-SEA models of a full vehicle that were built, sample experimental validation results of interior Sound Pressure Levels and panel vibration are shown for both bare and fully trimmed vehicle configurations.

Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function.

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

Experimental Investigation into the Drag Performance of Chevron-Shaped Protrusions in Wall-Bounded Turbulence

Chevron-shaped protrusions have been proposed in the literature for turbulent skin friction reduction. However, there is no consensus on the performance of this passive flow control technique; both an increase and a decrease in drag have been observed in previous studies. There is also no experimental evidence to support the working mechanism behind the drag reduction effect that has been postulated in the literature. In this study, direct force measurements were used to replicate experiments from the literature and, in addition, were used to test new array configurations to characterise the effect of individual design parameters on drag performance. A total of 23 different protrusion configurations were investigated in a turbulent boundary layer flow. In addition to the integral force measurements, particle image velocimetry was used to measure wall-parallel velocity fields in order to extract the statistical sizing and energy of the near-wall cycle turbulence. All configurations increased the drag between 2% and 10% for a friction Reynolds number of 1700. The drag reduction reported in the literature could not be replicated; however, these findings agreed with an experimental and numerical study that reported drag increase. The trend observed in the low-speed streak spacing from the PIV experiments was consistent with that observed in the balance data. Nevertheless, no evidence was found to support the working mechanism proposed in the literature. These results cast doubt on the proposed drag reduction potential of chevron-shaped protrusions. In the authors’ view, the results of this study strengthen previous conclusions regarding their minor increase in drag. Future studies to further approach a consensus are proposed.

Estimation of Response of Composite Shells to Acoustic Excitation Using Sea and Its Experimental Verification

Shells made up of composite material are extensively used in aerospace structures as they offer excellent specific strength and stiffness properties over their metallic counterparts. These elements experience high frequency and high intensity acoustic loads during the launch. Response behaviour of structures in the high frequency region is analyzed in a Statistical Energy Analysis (SEA) framework. Though the methodology of estimating the response using SEA exists, its applications to composite cylindrical shells and how they compare with experimental results are seldom reported. Present work fills this gap. Acceleration and strain responses of a typical composite sandwich cylinder under diffused acoustic field are estimated in SEA framework. Modal densities and coupling loss factors are estimated using expressions. The dissipation loss factors are determined experimentally. Same cylinder is subjected to diffused acoustic field in reverberation chamber and the accelerations and strains are measured. The work presents how much the theoretically estimated responses compare with the experimentally obtained results. It is seen that the acceleration responses compare reasonably well, but needs improvement. The strain-velocity relations compare very well. Acceleration and strain bounds such that 99 % of all measurements are within are also predicted which could be used in the structural design.

Study on Sound Transmission across a Floating Floor in a Residential Building by Using SEA

For the purpose of reducing the impact noise transmission across floating floors in residential buildings, two main sound transmission paths in the floating floor structure are considered: the stud path and the cavity path. The sound transmission of each path is analysed separately: the sound transmission through the cavity and the stud are predicted by statistical energy analysis (SEA). Then, the sound insulation prediction model of the floating floor is established. There is reasonable agreement between the theoretical prediction and measurement, and the results show that a resilient layer with low stiffness can attenuate the sound bridge effect, resulting in higher impact noise insulation. Then, the influences of the floor covering, the resilient layer and the floor plate on the impact sound insulation are investigated to achieve the optimised structure of the floating floor based on the sound insulation.

An Improved MSA Model for Evaluating the Sound Transmission Loss of a Rectangular Plate for Diffuse Field Incidence

This paper presents an approximate analytical model for estimating the transmission loss (TL) of a finite rectangular plate in the low frequency range, which is based on the modal summation approach (MSA) taking into account the modal radiation impedance and fluid loading. The mode-dependent radiation resistance is calculated using the Rayleigh integral. The fluid loading is taken into account through the natural frequency modified by the added mass. The results are compared with the ones of Statistical Energy Analysis (SEA) coupled with FEM and FEM coupled with BEM. In addition, the effects of the various vibration modes and the fluid loading on TL, and a way for reducing the calculation time are discussed.

Supporting VIP aircraft modeling using node symmetry and non-VIP assets

When developing acoustic and thermal solutions for private and commercial aircraft, extensive time and effort goes into the creation of models to support an optimized package that both delivers results and minimizes weight. Statistical Energy Analysis (SEA) is often used to model aircraft interior sound fields as it becomes applicable in the frequency ranges of interest to most passengers and engineers. This analysis is particularly sensitive to room dimensions and therefore ample research is done to ensure the model is as accurate as possible and historical data from similar aircraft can be used to calibrate the model before use. This takes a considerable amount of time and is often done early in the development stage of the aircraft. So how can one implement this same approach for VIP aircraft where the time to installation is measured in weeks and the aircraft interior is tailor-made making calibration with historical data difficult? This paper looks at ways to optimize the modeling process using symmetry in smaller subsections of the aircraft based on previously calibrated system and material measurements to help greatly reduce model creation time and increase model precision within the VAOne(tm) modeling suite.