Monte Carlo Module Research Articles

Strategic design of a rare trigonal symmetric luminescent covalent organic framework by linker modification

We designed a trigonal symmetric imine-linked covalent organic framework (COF), TFPC-DAB, with control over the angularity of the building units, where a bent C2-symmetric diamine, such as 1,3-diaminobenzene (1,3-DAB or DAB), with an exo-angle of 120° was used instead of those with an exo-angle of 180°, in combination with a C3-symmetric trialdehyde, such as tri(4-formylphenoxy)cyanurate (TFPC). Its synthesis was accomplished by reacting the building units in a mixture of mesitylene/dioxane/6 M acetic acid under solvothermal conditions. The phase purity, thermal stability, and porosity of TFPC-DAB were established by various analytical techniques. Utilizing the Density Functional Tight Binding (DFTB+) simulation and Pawley refinement, the best fit of the small angle x-ray pattern was found to have an AA stacking of TFPC-DAB in the trigonal space group P3 with low refinement parameters. Such smart materials are in huge demand to detect hazardous corrosive chemicals, such as HCl and NH3. The dual features of electron deficient π-acidic triazine moiety and heteroatoms (N/O) from TFPC and electron rich phenyl units from DAB embodied in the framework enhance its luminescent property and thereby make it suitable for solvent-based HCl and NH3 sensing. The detection limits for HCl and NH3 in methanol were found to be 14 and 82 ppb, respectively. The effect of solvent polarity on the sensing studies was observed with much lower detection limits in dioxane: 2.5 and 11 ppb for HCl and NH3, respectively. A detailed theoretical calculation using density functional theory and configurational bias Monte Carlo modules was conducted for understanding interactions between the COF and HCl or NH3 analytes.

A new 2D fluid-MC hybrid approach for simulating nonequilibrium atmospheric pressure plasmas: density distribution of atomic oxygen in radio-frequency plasma jets in He/O2 mixtures

A spatially two dimensional fluid-MC hybrid (fluid-kinetic) simulation method is developed and applied to the COST reference microplasma jet operated in helium with an oxygen admixture of 0.5%, excited by a single frequency voltage waveform with f = 13.56 MHz and ϕrms=275 V. The simulation approach is based on a fluid model augmented by a Monte Carlo module that generates electron impact rates for the continuity equations solved by the fluid module. This method is capable of providing the same level of accuracy as PIC/MCC simulations with an agreement within 5%–10% at atmospheric pressure, while being significantly faster (with a speedup factor of 30 for serial to 50 for parallel implementation). The simulation results are compared to previous measurements of atomic oxygen densities (Steuer et al 2021 J. Phys. D: Appl. Phys. 54 355204), and show a very good agreement. It is found that the buildup and saturation of the atomic oxygen density distribution along the jet are due to the interplay of chemical and electron impact reactions as well as of the gas flow. Comparing the simulation results to that of Liu et al 2021 J. Phys. D: Appl. Phys. 54 275204, it is inferred that fluid models where a 2-term BE solver is used, fail to describe the COST jet in an accurate manner due to the underestimation of the electron impact rates.

Plasma propulsion modeling with particle-based algorithms

This Perspective paper deals with an overview of particle-in-cell/Monte Carlo collision models applied to different plasma-propulsion configurations and scenarios, from electrostatic (E×B and pulsed arc) devices to electromagnetic (RF inductive, helicon, electron cyclotron resonance) thrusters, as well as plasma plumes and their interaction with the satellite. The most important items related to the modeling of plasma–wall interaction are also presented. Finally, the paper reports new progress in the particle-in-cell computational methodology, in particular, regarding accelerating computational techniques for multi-dimensional simulations and plasma chemistry Monte Carlo modules for molecular and alternative propellants.

Hybrid simulation of the plasma characteristics in a dual‐frequency dual‐antenna inductively coupled plasma discharge

AbstractDual‐frequency (DF) dual‐antenna inductively coupled plasma (ICP) sources have been proposed as one of the methods to produce large‐area uniform plasma. In this study, a hybrid model, consisting of a fluid module and an electron Monte Carlo (eMC) module, is used to further investigate the modulation of the plasma characteristics (i.e., induced electric field, electron energy distribution, electron heating mechanism, electron temperature, and plasma density) by the low‐frequency (LF) and high‐frequency (HF) currents in a DF argon discharge. When the inner LF current increases from 12 to 22 A at a fixed outer HF current of 10 A, the induced electric field is strengthened near the LF coil and weakened near the HF coil. The fraction of high‐energy electrons at different spatial positions increases with the increase of LF current, which is caused by the sufficient collision heating in the skin layer of LF source, leading to an increase in electron temperature. When the outer HF current rises from 7 to 13 A with the inner LF current fixed at 17 A, the induced electric field is enhanced near the HF coil and weakened near the LF coil. The fraction of low‐energy electrons at different positions increases with the increase of HF current since the energetic electrons from the skin layer of HF source are cooled due to the ionization, excitation, and stepwise ionization collisions between electrons and neutral particles, as well as the weakened induced electric field near the LF coil. As a result, the electron temperature drops with the increase of HF current. Moreover, when the LF and HF currents increase, the plasma density increases, and the distribution of plasma density is also significantly modulated.

Performance assessment of variant UNet-based deep-learning dose engines for MR-Linac-based prostate IMRT plans

Objective. UNet-based deep-learning (DL) architectures are promising dose engines for traditional linear accelerator (Linac) models. Current UNet-based engines, however, were designed differently with various strategies, making it challenging to fairly compare the results from different studies. The objective of this study is to thoroughly evaluate the performance of UNet-based models on magnetic-resonance (MR)-Linac-based intensity-modulated radiation therapy (IMRT) dose calculations. Approach. The UNet-based models, including the standard-UNet, cascaded-UNet, dense-dilated-UNet, residual-UNet, HD-UNet, and attention-aware-UNet, were implemented. The model input is patient CT and IMRT field dose in water, and the output is patient dose calculated by DL model. The reference dose was calculated by the Monaco Monte Carlo module. Twenty training and ten test cases of prostate patients were included. The accuracy of the DL-calculated doses was measured using gamma analysis, and the calculation efficiency was evaluated by inference time. Results. All the studied models effectively corrected low-accuracy doses in water to high-accuracy patient doses in a magnetic field. The gamma passing rates between reference and DL-calculated doses were over 86% (1%/1 mm), 98% (2%/2 mm), and 99% (3%/3 mm) for all the models. The inference times ranged from 0.03 (graphics processing unit) to 7.5 (central processing unit) seconds. Each model demonstrated different strengths in calculation accuracy and efficiency; Res-UNet achieved the highest accuracy, HD-UNet offered high accuracy with the fewest parameters but the longest inference, dense-dilated-UNet was consistently accurate regardless of model levels, standard-UNet had the shortest inference but relatively lower accuracy, and the others showed average performance. Therefore, the best-performing model would depend on the specific clinical needs and available computational resources. Significance. The feasibility of using common UNet-based models for MR-Linac-based dose calculations has been explored in this study. By using the same model input type, patient training data, and computing environment, a fair assessment of the models’ performance was present.

TUNING AND VALIDATION OF THE NEW RADCALC 3D MONTE CARLO BASED PRE-TREATMENT PLAN VERIFICATION TOOL

Verification procedures for patient-specific quality assurance (QA) in advanced radiotherapy are laborious and time-consuming. Moreover, it has been shown that these procedures cannot detect some inaccuracies for some particular complex cases due to tissue inhomogeneity and highly modulated plans. Secondary dose calculation verification of radiotherapy plans is an important aspect in patient-specific QA. A suitably optimized software, RadCalc equipped with 3D Monte Carlo Module (MC), was used to dosimetrically verify radiotherapy treatment plans where the measured dose distributions can be inaccurate due to the TPS dose calculation algorithm and/or treatment unit delivery uncertainties. MC Models were built using specific commissioning measurements and then the Additional Radiation to Light Field Offset parameter (Dosimetric Leaf Gap parameter) was tuned to achieve the best dose comparison agreement with phantom patient-specific QA measurements. The results showed a good agreement between the TPS and the simulations. RadCalc MC also allows to better estimate the plan doses in lung cancer patients and to detect the presence of possible inaccuracies due to tissue inhomogeneity, which is not estimable with a homogeneous phantoms.

HALO3D: An All-Mach Approach to Hypersonic Flows Simulation, Part II

ABSTRACT This paper is Part II of the two-paper series ‘HALO3D: An All-Mach Approach to Hypersonic Flows Simulation’ presenting advances in a monolithic flow simulation software for the design of high-speed aircraft. Part II highlights the methodology behind the continuum regime finite-rate surface chemistry, weak-ionisation, and rarefied regime Direct Simulation Monte Carlo modules. Validation and benchmark simulations of flows over a 2D cylinder, the 3D RAM-C II geometry, and the 3D Lockheed Martin X-33 geometry are provided. Within HALO3D, a Hessian-based multiphysics automatic mesh adaptation methodology yields optimal solutions that ought to provide valuable benchmarks for verifying other hypersonic flow simulation tools. Such mesh optimisation automatically generates a common truly mesh-independent solution across the continuum and rarefied regimes, taking into account various thermo-chemical models. These developments establish HALO3D as a mature, readily extendible software package for hypersonic aircraft analysis.

Evaluation of adsorption and mechanical strength of 13X zeolite mixtures with phyllosilicate binders using molecular dynamics simulation and positron annihilation spectroscopy

There is growing interest in developing zeolites with novel internal structures that have optimal adsorptive capacity and high mechanical strength, while offering advantages, such as being light weight. We integrate computational and experimental methods to explore the effect of binder/zeolite types, and weight percentages on the mechanical strength of 13X zeolite and adsorption capacities of N2, H2O, and CO2 for additive manufacturing (AM) applications with the goal of maximizing both adsorption and strength. Zeolite 13X mixtures and phyllosilicate binders (either bentonite or kaolin) are combined using molecular dynamics (MD) simulations to create structures with various binder/zeolite weight percentages. Adsorption capabilities and mechanical strength are assessed using the grand canonical Monte Carlo (GCMC) and ReaxFF modules, respectively. Our modeling shows that an optimized zeolite/binder ratio for N2 adsorption is around 15 wt% for kaolin and roughly 10 wt% for bentonite. The resulting parameters can be applied to facilitate macro-scale computational fluid dynamics (CFD) and finite element method (FEM) simulations of an AM zeolite structure. We also performed Positron Annihilation Lifetime Spectroscopy (PALS) measurements on zeolite samples to explore the effect of changes in the internal volume. The results show an inverse relationship between the free volume and the solid loading. Adding a binder changes the morphology of the zeolite-binder compound and decreases open-volume area significantly.

Developing a Reactivity-Equivalent Physical Transformation to Simulate an Axially Heterogeneous Boiling Water Reactor

Hitachi is advancing their designs for a conceptual reactor called the resource-renewable boiling water reactor (RBWR), a concept reactor similar to the advanced boiling water reactor with a harder neutron spectrum. This design aims to minimise construction costs and waste production as well as to utilise separated plutonium and minor actinide fuel. However, the axial heterogeneity of the design poses calculation difficulties. The aim of this work is to use a known method, reactivity-equivalent physical transformation (RPT), for calculating fuel with double heterogeneity and apply it to a BWR-type fuel pin. This could reduce the calculation time needed for optimisation of the design of the RBWR. The objective of the study is to use SCALE 6.2 to produce an equivalent axial pin model by comparison with the burnup and neutron spectra of a radial model of the fuel. This model can then be used for 2D burnup calculations, and in future work will be used for the generation of two-group and multigroup cross-sections for further deterministic calculations as part of a two-step approach for analysis of the RBWR. The RPT method has been extensively tested on spherical fuel, and SCALE is a standard industry code. The initial radial model is a hexagonal assembly with 20% enriched UO2 fuel in a zircaloy cladding, surrounded by light water moderator. The derived axial model has a water density distribution taken from Hitachi’s RBWR designs. Criticality over 70 GWd/tU burnup is estimated using the model. The application of the RPT to the BWR pin was shown to be possible, but to have limitations with the introduction of additional radial complexity. For a single pin, excellent agreement between the radial and axial models could be found across a range of water densities, but in the case of an assembly level calculation distinct equivalence models were required for each water density. In addition, the produced RPT model is validated using SCALE’s 3D Monte Carlo module, KENO.

An ultra-fast deep-learning-based dose engine for prostate VMAT via knowledge distillation framework with limited patient data

Objective. Deep-learning (DL)-based dose engines have been developed to alleviate the intrinsic compromise between the calculation accuracy and efficiency of the traditional dose calculation algorithms. However, current DL-based engines typically possess high computational complexity and require powerful computing devices. Therefore, to mitigate their computational burdens and broaden their applicability to a clinical setting where resource-limited devices are available, we proposed a compact dose engine via knowledge distillation (KD) framework that offers an ultra-fast calculation speed with high accuracy for prostate Volumetric Modulated Arc Therapy (VMAT). Approach. The KD framework contains two sub-models: a large pre-trained teacher and a small to-be-trained student. The student receives knowledge transferred from the teacher for better generalization. The trained student serves as the final engine for dose calculation. The model input is patient computed tomography and VMAT dose in water, and the output is DL-calculated patient dose. The ground-truth \\dose was computed by the Monte Carlo module of the Monaco treatment planning system. Twenty and ten prostate cases were included for model training and assessment, respectively. The model’s performance (teacher/student/student-only) was evaluated by Gamma analysis and inference efficiency. Main results. The dosimetric comparisons (input/DL-calculated/ground-truth doses) suggest that the proposed engine can effectively convert low-accuracy doses in water to high-accuracy patient doses. The Gamma passing rate (2%/2 mm, 10% threshold) between the DL-calculated and ground-truth doses was 98.64 ± 0.62% (teacher), 98.13 ± 0.76% (student), and 96.95 ± 1.02% (student-only). The inference time was 16 milliseconds (teacher) and 11 milliseconds (student/student-only) using a graphics processing unit device, while it was 936 milliseconds (teacher) and 374 milliseconds (student/student-only) using a central processing unit device. Significance. With the KD framework, a compact dose engine can achieve comparable accuracy to that of a larger one. Its compact size reduces the computational burdens and computing device requirements, and thus such an engine can be more clinically applicable.

BTE-Barna: An extension of almaBTE for thermal simulation of devices based on 2D materials

We present BTE-Barna (Boltzmann Transport Equation - Beyond the RTA for NAnosystems), a software package that extends the Monte Carlo (MC) module of the almaBTE solver of the Peierls-Boltzmann transport equation for phonons (PBTE) to work with nanosystems based on 2D materials with complex geometries. To properly capture how the phonon occupations evolve in momentum space as a result of scattering, we have supplemented the relaxation-time approximation with an implementation of the propagator for the full linearized version of the PBTE. The code can now find solutions for finite and extended devices under the effect of a thermal gradient, with isothermal reservoirs or with an arbitrary initial temperature distribution in space and time, writing out the temperature and heat flux distributions as well as their spectral decompositions. Besides the full deviational MC solver, a number of useful approximations for highly symmetric devices are also included. Program summaryProgram Title::BTE-BarnaCPC Library link to program files:https://doi.org/10.17632/t58z289tpc.1Developer's repository link:https://github.com/sousaw/BTE-BarnaCode Ocean capsule:https://codeocean.com/capsule/4171806Licensing provisions: Apache-2.0Programming language: C++Nature of problem: Calculation of temperature profiles, thermal flux and/or effective thermal conductivities for nanosystems based on 2D materials.Solution method: For highly symmetric systems confined along some direction(s), the linearized phonon Boltzmann transport equation is solved iteratively by partially suppressing the phonon lifetimes due to boundaries. For more complex geometries, an energy-based deviational Monte Carlo method including off-diagonal terms in the linearized scattering operator arising from the phonon-phonon interaction is used.Additional comments including restrictions and unusual features: Depends on the following external libraries: boost, Eigen, HDF5, MessagePack, MPI, oneTBB, spglib, and almaBTE.

PICSAR-QED: a Monte Carlo module to simulate strong-field quantum electrodynamics in particle-in-cell codes for exascale architectures

Physical scenarios where the electromagnetic fields are so strong that quantum electrodynamics (QED) plays a substantial role are one of the frontiers of contemporary plasma physics research. Investigating those scenarios requires state-of-the-art particle-in-cell (PIC) codes able to run on top high-performance computing (HPC) machines and, at the same time, able to simulate strong-field QED processes. This work presents the PICSAR-QED library, an open-source, portable implementation of a Monte Carlo module designed to provide modern PIC codes with the capability to simulate such processes, and optimized for HPC. Detailed tests and benchmarks are carried out to validate the physical models in PICSAR-QED, to study how numerical parameters affect such models, and to demonstrate its capability to run on different architectures (CPUs and GPUs). Its integration with WarpX, a state-of-the-art PIC code designed to deliver scalable performance on upcoming exascale supercomputers, is also discussed and validated against results from the existing literature.

SimMEEG software for simulating event-related MEG and EEG data with underlying functional connectivity

BackgroundSimulating electromagnetic brain signals is becoming more and more prevalent because of the need to verify and validate electrophysiological analysis methods, especially with regards to measurements of functional connectivity and phase-amplitude coupling (PAC). New MethodA Matlab software package with a graphical user interface, called SimMEEG, was developed so that novice and expert users could input basic signal parameters (e.g., multiple frequencies, amplitudes, durations, phase interactions, and phase-amplitude couplings) in order to simulate signals that approximate event-related brain activity. A major objective for developing this software was to create software to simulate interacting brain signals that could be used to verify functional connectivity analyses, specifically phase-locking value (PLV), phase-lag index (PLI) algorithms, and PAC. SimMEEG simulates three sources with various parameters defined by users. Users can define, multiple frequencies, event-related changes in amplitude, synchronizations (i.e., PLV and PLI), and PAC. ResultsSimMEEG simulated signals that estimated the signal input parameters within specific target limits for PLV, PLI, and PAC. In addition, SimMEEG’s synthetic signals closely approximated real human visual event-related responses. The Monte-Carlo module was used to simulate ten MEG and EEG datasets, conduct function connectivity analyses for comparison among several inverse solutions, and generate errors from ground truth. SimMEEG identified inverse solutions that had good and poor performances for functional connectivity analyses. Comparison with existing methodsComparison between other simulation methods and SimMEEG are discussed. ConclusionSimMEEG software could be used to simulate real human EEG and MEG to aid in verification of analysis pipelines by comparing analyzed results to the simulated ground truth of the user-defined event-related brain signals. A core program within SimMEEG, called SimSignals, might also be used in other fields that would benefit from simulations of three interacting signals.

A Kinetic Monte Carlo Study of Retention Time in a POM Molecule-Based Flash Memory

The modelling of conventional and novel memory devices has gained significant traction in recent years. This is primarily because the need to store an increasingly larger amount of data demands a better understanding of the working of the novel memory devices, to enable faster development of the future technology generations. Furthermore, in-memory computing is also of great interest from the computational perspectives, to overcome the data transfer bottleneck that is prevalent in the von-Neumann architecture. These important factors necessitate the development of comprehensive TCAD simulation tools that can be used for modeling carrier dynamics in the gate oxides of the flash memory cells. In this work, we introduce the kinetic Monte Carlo module that we have developed and integrated within the Nano Electronic Simulation Software (NESS) – to model electronic charge transport in Flash memory type structures. Using the developed module, we perform retention time analysis for a polyoxometalate (POM) molecule-based charge trap flash memory. Our simulation study highlights that retention characteristics for the POM molecules have a unique feature that depends on the properties of the tunneling oxide.

Ion energy and angular distributions in planar Ar/O2 inductively coupled plasmas: hybrid simulation and experimental validation

A hybrid model, which consists of a fluid module, a sheath module and an ion Monte Carlo module, is employed to investigate the dependence of ion energy and angular distributions (IEDs and IADs) on the inductively coupled plasma (ICP) power, pressure, gas ratio, bias power and bias frequency in Ar/O2 discharges. The results indicate that the bimodal distribution appears as bias power increases or bias frequency decreases. Moreover, the low and high energy peaks of IEDs move to higher energy with the rise of bias power and O2 content. Whereas, an opposite tendency is observed with the increase of ICP power and pressure. For IADs, it is clear that a larger percentage of ions incident on the electrode have a smaller deflection angle by increasing bias power or decreasing pressure, and a similar evolution is observed with the decline of bias frequency. Besides, the better collimation of ions is obtained at larger O2 concentration, but ICP power only has little influence on IADs. In order to validate the model, a comparison between the simulated IEDs and those measured by a retarding field energy analyzer has been done, and shows a good agreement. The results obtained in this work could help us to gain more insight into the dependence of IEDs and IADs on the discharge parameters, which is of significant importance in the improvement of the etching rate and anisotropy.

Quantitative modeling of neoclassical tearing mode driven fast ion transport in integrated TRANSP simulations

The TRANSP-‘Kick’ energetic particle transport model has been extended to study neoclassical tearing mode (NTM) driven fast ion (FI) transport with zero free parameters. FI transport induced by the NTM is obtained by calculating perturbed FI orbits via the guiding center particle following code orbit in the magnetic field of an experimentally characterized island chain. Transport probabilities are then used in TRANSP’s Monte Carlo module NUBEAM to modify the FI distribution in every time step of the full TRANSP analysis. This procedure retains all TRANSP functionality and self-consistently predicts the NTM impact on beam torque, current drive and heating. Comparisons to DIII-D steady state, hybrid and ITER baseline plasmas are encouraging with the model quantitatively recovering the measured neutron rates. FI effects depend on the location and width of FI phase space resonances which are not accounted for by the former ad hoc beam diffusivity model of TRANSP. Resonance overlaps result in a transport threshold, for the cases investigated herein, at . When the FI transport is dominated by energy and momentum redistribution. In particular, m/n = 3/2 NTMs lead to hollow profile at q = m/n and broadened profile in the core. When FI losses increase, the neutral beam torque () and driven current () decrease across the entire plasma. The effects on NB profiles can strongly depend on the NTM frequency and mode numbers with the 3/2 (2/1) broadening (peaking) near the magnetic axis.

Range versus density dependence of proton-therapy beams in human tissues

Abstract Range versus density dependence of proton-therapy beams in human tissues is discussed in the paper. The main attention is paid to energy-scaling of simple fitting functions that fit the range-to-density dependence for single proton-beam energies. These functions have been found by fitting ranges obtained with the aid of the Monte Carlo module of SRIM2013. The simulations have been run for 28 human tissues at 8 energies from 60 MeV to 220 MeV. It has been shown that a fitting function that depends solely on the target density can be found at each proton-beam energy. After the energy-scaling, the fitted ranges have been compared with the Monte Carlo ranges. We conclude that the final energy-scaled fitting function provides ranges within 0.4% of the Monte Carlo range (on average), which is less than 30% of the natural range-straggling (1.4% of the Monte Carlo range on average). The worst data-point has an off-set from the fitting function less than 1.7% of the Monte Carlo range.

Theory of radiative electron polarization in strong laser fields

Radiative polarization of electrons and positrons through the Sokolov-Ternov effect is important for applications in high-energy physics. Radiative spin-polarization is a manifestation of quantum radiation reaction affecting the spin-dynamics of electrons. We recently proposed that an analogue of the Sokolov-Ternov effect could occur in the strong electromagnetic fields of ultra-high-intensity lasers, which would result in a build-up of spin-polarization in femtoseconds. In this paper we develop a density matrix formalism for describing beam polarization in strong electromagnetic fields. We start by using the density matrix formalism to study spin-flips in non-linear Compton scattering and its dependence on the initial polarization state of the electrons. Numerical calculations show a radial polarization of the scattered electron beam in a circularly polarized laser, and we find azimuthal asymmetries in the polarization patterns for ultra-short laser pulses. A degree of polarization approaching 9 % is achieved after emitting just a single photon. We develop the theory by deriving a local constant crossed field approximation (LCFA) for the polarization density matrix, which is a generalization of the well known LCFA scattering rates. We find spin-dependent expressions that may be included in electromagnetic charged-particle simulation codes, such as particle-in-cell plasma simulation codes, using Monte-Carlo modules. In particular, these expressions include the spin-flip rates for arbitrary initial polarization of the electrons. The validity of the LCFA is confirmed by explicit comparison with an exact QED calculation of electron polarization in an ultrashort laser pulse.

Design of district heating networks through an integrated thermo-fluid dynamics and reliability modelling approach

This paper aims to describe a newly developed analysis tool for supporting District Heating Networks (DHNs) grid design and energy-reliability optimization procedures. The tool couples a Thermo-fluid dynamic Module to simulate the energy and physical behaviour of the analysed network, and a Monte Carlo Module to manage grid failure and repair processes: as a consequence, it is able to investigate both the fluid dynamics and the reliability aspects of the entire network, from the production plants to the final users.The tool can also take into account the effect of thermal energy storages (TESs), whose installation along the grid could be beneficial not only to improve the efficiency and the management of the network, but also to increase the quality of the service and its reliability, reducing the number of hours of service disruption in the case of system failures. It thus allows evaluating the effects on both load coverage and overall network availability of different grid configurations assuming alternative size and location of TES systems.The methodology has been applied for testing purposes to a case study representative of a portion of an Italian district heating network, focusing on three different scenarios and several possible alternative grid configurations.

High-efficiency detector of secondary and backscattered electrons for low-dose imaging in the ESEM

A new Combined System for high-efficiency detection of Secondary and Backscattered Electrons (CSSBE) in the ESEM consists of three detectors: an ionisation SE detector, an improved scintillation BSE detector, and a new Ionisation Secondary Electron Detector with an electrostatic Separator (ISEDS). The ISEDS optimizes conditions for electron-gas ionisation phenomena in the ESEM to achieve a strongly amplified signal from the secondary electrons with a minimal contribution from backscattered and beam electrons. For this purpose, it is originally equipped with an electrostatic separator, which focuses signal electrons towards a detection electrode and controls the concentration of positive ions above the sample. The working principle of the ISEDS is explained by simulations of signal electron trajectories in gas using the EOD program with our Monte Carlo module. The ability to detect the signal electrons in a selected range of energies is described with Geant4 Monte Carlo simulations of electron-solid interactions and proven by experimental results. High-efficiency detection of the ISEDS is demonstrated by imaging a low atomic number sample under a reduced beam energy of 5 keV, very low beam currents of up to 0.2 pA, and gas pressure of hundreds of Pa.