USING THE UNIVERSITY'S ENERGY EFFICIENCY KNOWLEDGE HUB FOR ENERGY RENOVATION AND ENERGY MODERNIZATION OF UNIVERSITY BUILDINGS

BACKGROUND AND OBJECTIVES. The most important socio-economic task in the current period is to transfer Ukraine's economy to an intensive way of development in order to improve the level and quality of life of the population and solve the full range of social problems. Implementation of such a policy dictates the need to solve problems of reconstruction and modernization of buildings and structures, primarily related to the public sphere (including higher education institutions), in order to eliminate the existing inconsistency of the technical condition and functional and consumer qualities of public buildings to current standards and consumer requirements. Therefore, one of the most urgent directions of development of higher educational institutions is the task of providing effective overhaul and reconstruction of buildings, increasing their energy efficiency.METHODS. The theoretical and methodological basis of the study were the fundamental and applied developments of leading domestic and foreign scientists in the theory and practice of management of energy modernization and energy reconstruction of buildings, increasing energy efficiency of buildings. The factual basis of research were the legislative acts of Ukraine in the field of energy efficiency, normative and methodical documents on the modernization and reconstruction of buildings, Directive 2010/31/EC in the field of energy saving. When solving specific tasks the methods of system and comparative analysis, economic-mathematical methods of efficiency evaluation of energy reconstruction and energy modernization projects were used.FINDINGS. The method of calculation of the reduced resistance to heat transfer of the enclosing structures and the shell of the 4th building of Kyiv National University of Technology and Design as a whole taking into account the temperature and humidity conditions in the fencing marginal zones. It is shown that in the enclosure edge zones the heat protective properties decrease resulting in a deterioration of the heat protection of the whole building. Practical recommendations for the design of fencing structures of modern buildings taking into account the temperature-moisture regime are proposed.CONCLUSION. For the analysis of complex processes of moisture transfer in enclosures, a mathematical model based on the moisture potential is most convenient. A certain difference from the thermal potential (temperature) to the definition of the moisture potential allows to diagnose the most general assessment of the moisture regime of exterior and interior fences on the basis of HUB knowledge on energy efficiency. At use of this model it is possible to consider process of moisture exchange in a wide range of humidity and temperature taking into account movement of a moisture as a basis of carrying out energy reconstruction and energy modernization of operating buildings of the university.

Application of ASAS method to PAMELA calorimeter

In this paper, we propose a method that makes it possible to to improve energy reconstruction for data obtained via thin heterogeneous calorimeters for direct measurements of cosmic rays with energies of TeV and higher. Despite the large number of modern experimental complexes, the primary energy of cosmic nuclei with energies above 1 TeV is determined with large errors associated with fluctuations in the development of the cascade. For heterogeneous calorimeters, transient effects give an additional negative effect. In this paper we analyze the main causes of fluctuations and discuss a method for reducing the effect of fluctuations on the results of primary energy reconstruction. The method of accumulation of signal along the spectrum (ASAS) is used to reduce fluctuations associated with transient effects. The method was tested using the heterogeneous calorimeter of the PAMELA collaboration. It is shown that the proposed approach makes it possible to correctly determine the energy of slowly developing showers, the maxima of which are not measured.

Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks

The liquid argon ionization current in a sampling calorimeter cell can be analyzed to determine the energy of detected particles. In practice, experimental artifacts such as pileup and electronic noise make the inference of energy from current a difficult process. The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut-down of 2025–2027. Signal processing techniques that are used to extract the energy of detected particles in the ATLAS detector will suffer a significant loss in performance under these conditions. This paper compares the presently used optimal filter technique to convolutional neural networks for energy reconstruction in the ATLAS liquid argon hadronic end cap calorimeter. In particular, it is shown that convolutional neural networks trained with an appropriately tuned and novel loss function are able to outperform the optimal filter technique.

Reconstruction of DBI-essence dark energy with f(R) gravity and its effect on black hole and wormhole mass accretion

In this paper, we have used the reconstructed Dirac–Born–Infeld (DBI)-essence dark energy density to modify the mass accretions of black holes and wormholes. In general, the black hole mass accretion does not depend on the metric or local Einstein geometry. That is why we have used a generalized mechanism by reconstructing the DBI-essence dark energy reconstruction with [Formula: see text] gravity. We have used some particular forms of the scale factor to analyze the accretion phenomena. We have shown the effect of cosmic evolution in the proper time variation of black hole mass accretion. Finally, we have studied the validity of energy conditions and analyzed the Type I–IV singularities for our reconstructed model.

On the use of neural networks for energy reconstruction in high-granularity calorimeters

We contrasted the performance of deep neural networks — Convolutional Neural Network (CNN) and Graph Neural Network (GNN) — to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.

Direct Measurements of Cosmic Rays (TeV and beyond) Using an Ultrathin Calorimeter: Lessening Fluctuation Method

In this paper, we propose a method that makes it possible to use an ultrathin calorimeter for direct measurements of cosmic rays with energies of TeV and higher. The problems of determining the primary energy with a thin calorimeter, due to large fluctuations in shower development, the low statistics of analyzed events and the large size required for the calorimeter, are considered in detail. A solution to these problems is proposed on the basis of a lessening fluctuation method. This method is based on the assumption of the universality of the development of cascades initiated by particles of the same energy and mass. For energy reconstruction, so-called correlation curves are used. The main analyzed quantities are the size of the cascade and the rate of its development. The method was tested using the calorimeter of the PAMELA collaboration. Based on simulations, it is shown that the primary energy can be determined on the ascending branch of the cascade curve. This fact solves the problems associated with the need to increase the calorimeter thickness with an increase in primary energy and with the limitation of the analyzed events. The proposed technique is universal for different energies and different nuclei.

Artificial Neural Networks on FPGAs for Real-Time Energy Reconstruction of the ATLAS LAr Calorimeters

The ATLAS experiment at the Large Hadron Collider (LHC) is operated at CERN and measures proton–proton collisions at multi-TeV energies with a repetition frequency of 40 MHz. Within the phase-II upgrade of the LHC, the readout electronics of the liquid-argon (LAr) calorimeters of ATLAS are being prepared for high luminosity operation expecting a pileup of up to 200 simultaneous proton–proton interactions. Moreover, the calorimeter signals of up to 25 subsequent collisions are overlapping, which increases the difficulty of energy reconstruction by the calorimeter detector. Real-time processing of digitized pulses sampled at 40 MHz is performed using field-programmable gate arrays (FPGAs). To cope with the signal pileup, new machine learning approaches are explored: convolutional and recurrent neural networks outperform the optimal signal filter currently used, both in assignment of the reconstructed energy to the correct proton bunch crossing and in energy resolution. The improvements concern in particular energies derived from overlapping pulses. Since the implementation of the neural networks targets an FPGA, the number of parameters and the mathematical operations need to be well controlled. The trained neural network structures are converted into FPGA firmware using automated implementations in hardware description language and high-level synthesis tools. Very good agreement between neural network implementations in FPGA and software based calculations is observed. The prototype implementations on an Intel Stratix-10 FPGA reach maximum operation frequencies of 344–640 MHz. Applying time-division multiplexing allows the processing of 390–576 calorimeter channels by one FPGA for the most resource-efficient networks. Moreover, the latency achieved is about 200 ns. These performance parameters show that a neural-network based energy reconstruction can be considered for the processing of the ATLAS LAr calorimeter signals during the high-luminosity phase of the LHC.

Calibration of the Gerda experiment

The GERmanium Detector Array (Gerda) collaboration searched for neutrinoless double-$$\beta $$ β decay in $$^{76}$$ 76 Ge with an array of about 40 high-purity isotopically-enriched germanium detectors. The experimental signature of the decay is a monoenergetic signal at $$Q_{\beta \beta }$$ Q β β $$=2039.061(7)$$ = 2039.061 ( 7 ) keV in the measured summed energy spectrum of the two emitted electrons. Both the energy reconstruction and resolution of the germanium detectors are crucial to separate a potential signal from various backgrounds, such as neutrino-accompanied double-$$\beta $$ β decays allowed by the Standard Model. The energy resolution and stability were determined and monitored as a function of time using data from regular $$^{228}$$ 228 Th calibrations. In this work, we describe the calibration process and associated data analysis of the full Gerda dataset, tailored to preserve the excellent resolution of the individual germanium detectors when combining data over several years.