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Energy crops on marginal lands are seen as an interesting option to increase biomass contribution to the primary energy mix. However, in the literature there is currently a lack of integrated assessments of margin land availability, energy crop production potential and supply chain optimisation. Assessing the potential and the cost of these resources in a given region is therefore a difficult task. This work also emphasises the importance on a clear definition and discussion about marginal lands and the related ethical issues embedded in the concept to ensure positive societal impacts of the results. This study proposes a methodology to estimate and analyse, in terms of economic costs, the potential of miscanthus grown on marginal lands from the production to the final point of use. Different datasets are assembled and a supply chain optimisation model is developed to minimize the total cost of the system. Miscanthus is used as a representative energy crop for the Belgian and French case studies. High temperature heat demand is considered as final use. The miscanthus can be traded by truck either in the form of chips or pellets. The results show that the miscanthus on marginal lands could supply high temperature heat up to 38 TWh in France and 1.4 TWh in Belgium with an average cost of around 50 €/t. The different sensitivity analyses showed that the yield variation has the strongest influence on the final cost, together with the distances and the cost of production of miscanthus. The main pattern observed is the local consumption of miscanthus chips and export of the surplus (if any) to the neighbouring regions. Pellets are only of marginal interest for France and are never observed for Belgium. Distances and availability of sufficient feedstocks are the two main parameters impacting the production of pellets.

The data inversion algorithm of the Dekati electrical low pressure impactor (ELPI+) is the procedure to convert the electrical currents measured in each impactor stage into a particle size distribution. If a particle is collected in the incorrect stage, either due to particle bounce or premature collection, the erroneously induced current will cause an error to propagate through the data inversion. In this work, it is examined how this error propagation will modify the particle number size distribution. It is shown that particle bounce can contribute considerably to an erroneous particle distribution as one large bouncing particle can be misinterpreted as more than 10,000 small particles. This indicates that efforts should be made to avoid particle bounce in the ELPI+ as particle bounce thoroughly modifies the obtained particle number size distribution. On the other hand, it appears that the ELPI+ is quite robust against the effects of premature particle collection.

The model-based control of a boiler-turbine system (BTS) is a formidable task due to coupling in state variables, nonlinearities and constraints on the control inputs. In this paper, a model-based, multi-variable dynamic sliding mode control (DSMC) is designed for the nonlinear BTS model to maintain the drum pressure, electric power and water level at the desired levels. In DSMC, an implicit sliding manifold is designed for the water level due to its complexity and explicit dependence on the control inputs. For this purpose, an auxiliary function is computed, and the sliding mode is enforced in such a way that the system fluid density tracks the auxiliary function, and subsequently the water level tracks the desired trajectory. Owing to its complexity, the time derivatives of the auxiliary function are computed using the uniform robust exact differentiator (URED). An adaptive Kalman filter (AKF) is designed for the estimation of the unmeasurable state i.e., system fluid density. The design of AKF is based on the quasi-linear model of the BTS. Furthermore, a detailed stability analysis is carried out to ensure the boundedness of the closed-loop system. The simulation results depict that the designed control scheme exhibits the desired tracking performance in the presence of external disturbances, nonlinearities, constraints on the inputs, and measurement and process noises.

High-altitude propeller optimization aims to discover blade designs that yield superior performance in the low air density environment present at the lower levels of the stratosphere. In our work, a multi-fidelity, multi-objective (MFMO) optimization framework is developed and tested for the application of high-altitude propeller optimization. The optimization employs three levels of fidelity, including Vortex Theory and 3D RANS with the use of γ −Reθ transition model, converged with first-order upwind, and second-order upwind for the momentum equations. The Variable Fidelity Expected Improvement Matrix (VFEIM) is used as an MFMO acquisition function and it is extended to account for failed designs, batch submission of infill designs, and geometric filtering of airfoils. Moreover, a Hierarchical Kriging surrogate model is used to fuse the performance data from the three levels of fidelities. Due to the large number of low-fidelity data, a Kriging approach is proposed to fit large, high-dimensional data. The proposed model is found to perform better on the tested propeller performance dataset compared to other popular techniques, such as Sparse Gaussian Processes. Moreover, through a test function example, the benefit of an MFMO optimization approach is examined showing sensitivity to the cost ratio between the different fidelities. Finally, the propeller optimization results demonstrate that high-performing designs are achievable with the proposed optimization framework.

Ammonia can be an essential energy vector for storing excess renewable energy during the year and release this energy when demand is high. This storage during the year provides an essential part in the energy sector for moments when there is not enough wind and sun to cover the demand. Although the Haber-Bosch is a very mature process to synthesize hydrogen and nitrogen to ammonia, it is not flexible enough to match the erratic nature of renewables. This mismatch creates the need for large intermediate hydrogen buffer tanks. To reduce the capacity of the hydrogen storage tank, we need to optimize the dynamic power-to-ammonia process in a minute time scale while considering the uncertainties linked to renewables. Therefore, we coupled a stochastic wind power process to a dynamic Power-to-Ammonia process and performed a design optimization under uncertainties, i.e. a robust design optimization, for a three-hour wind scenario while considering optimal dynamic control. The robust design optimization showed significant trade-offs between the most productive, the most flexible, the most resilient, and robust power-to-ammonia plant designs. For the flexible and productive designs, the bypass fraction and buffer feed have essential roles in achieving these designs. In the future, we will use a stochastic process to consider the uncertainty of renewable power over more extended periods (days, weeks and months) coupled with the robust design optimization of the dynamic power-to-ammonia process and investigate its effect on the intermediate hydrogen buffer size and the plant’s cost.

Regions with abundantly available renewable energy are not necessarily the same as those with a high population density and energy consumption. Therefore, renewable energy can be produced in optimal climate conditions with a remote renewable hub and transported to these population-dense regions. To establish this energy transport to these regions, ammonia provides a flexible, easy-to-handle energy carrier. However, current literature rarely considers the impact of techno-economic uncertainty on the feasibility of this transport. Using those uncertainties, we performed a robust design optimization on the levelized cost of ammonia and the power-to-ammonia efficiency to compare the local (Belgium) and remote (Morocco) ammonia production and transport to Belgium. This paper provides the robust designs (i.e. least sensitive to uncertainty) for local and remote renewable ammonia production and the advantages of both approaches on the levelized cost and energy efficiency. The results confirm that ammonia production in regions with high solar irradiance followed by the transport of ammonia is cost-effective and robust (601 euro/tonne in mean and 98 euro/tonne in standard deviation) over local production (852 euro/tonne in mean and 139 euro/tonne in standard deviation). However, local ammonia production provides for more efficient (54.8% in mean) and less sensitive power-to-ammonia plant designs (0.16% in standard deviation), while the production in Morocco is less efficient (52.2% in mean) and more sensitive to uncertainties (0.39% in standard deviation). The capacity of the photovoltaic arrays and the electrolyzers highly influences both objectives. The sensitivity analysis shows that capital and operational expenses of the photovoltaics and electrolyzer stack dominate the designs with the lowest levelized cost in mean and standard deviation. However, the energy consumption uncertainty of the Haber–Bosch also impacts the cost of the lowest mean levelized cost. This uncertainty also dominates the designs with the highestenergy efficiency in mean and lowest standard deviation.

Quasi-static and dynamic stall control experiments, using dielectric barrier discharge plasma actuators, were performed on a square-tipped aspect-ratio-one wing, with a NACA 0015 profile, under harmonic pitching, at a Reynolds number of 3×105. Relatively low 풪(1) and relatively high 풪(10) pulse-modulated reduced excitation frequencies were introduced at the leading edge; the former produced the largest poststall lift improvements, whereas the latter produced the highest maximum lift. A reduced frequency, based on laminar separation bubble shedding, was enlisted to explain the differences between the two results. Under conditions of dynamic pitching, evidence of a dynamic stall vortex was absent, due to its interaction with the strong tip vortices, consistent with prior experimental and numerical investigations. As a general rule, performance benefits produced by excitation under quasi-static conditions were reproduced under harmonic pitching. Low-order dynamic stall and dynamic stall control models, based on a modified version of the Goman–Khrabrov delta-wing concept, were evaluated. Model constants were obtained from quasi-static data sets, and a single baseline dynamic case was employed to determine the time constants. The model produced excellent results for both baseline and controlled cases, which was attributed to similar lift and stall mechanisms exhibited by delta and rectangular low-aspect-ratio planforms.

Cascade impactors, such as the Dekati electrical low pressure impactor (ELPI+), are useful measuring devices as they can classify particles per size range based on their inertial behaviour. However, various mechanisms, such as particle loss and particle bounce, make the measured particle size distribution to diverge from the real distribution. Moreover, some of these mechanisms depend on the amount of particles that have already been collected in an impactor stage and consequently the collection efficiency of a stage may be time-dependent. In this work, such mechanisms are investigated and their impact on the performed measurements is assessed. The use of greased and ungreased aluminium collection substrates and porous sintered impactor plates were extensively tested in the ELPI+. The aluminium substrates, regardless of the initial greasing, show an exponential decrease in total number concentration and an exponential increase in geometric mean particle diameter with increasing particle load on the substrates. This is the result of an exponential concentration decrease of the stages that collect particles smaller than the accumulation peak and a concentration increase of the stages that collect larger particles. The effect is attributed to the decrease in particle bounce with increasing particle loading. On the other hand, the sintered impactor plates show barely any time-dependent collection behaviour.

This work discusses the experimental challenges and processing of unsteady experiments for a pitching wing in the low-speed wind tunnel of the Vrije Universiteit Brussel. The setup used for unsteady experiments consisted of two independent devices: (a) a position control device to steer the pitch angle of the wing, and (b) a pressure measurement device to measure the aerodynamic loads. The position control setup can pitch the wing for a range of frequencies, amplitude, and offset levels. In this work, a NACA-0018 wing profile was used with an aspect ratio of 1.8. The position control and the pressure measurement setups operate independently of each other, necessitating advanced signal processing techniques to synchronize the pitch angle and the lift force. Furthermore, there is a (not well-documented) issue with the (sampling) clock frequency of the pressure measurement setup, which was resolved using a fully automated spectral analysis technique. The wing was pitched using a simple harmonic sine excitation signal at eight different offset levels (between 6° and 21°) for a fixed amplitude variation (std) of 6°. At each offset level, the wing was pitched at five different frequencies between 0.1 Hz and 2 Hz (that correspond to reduced frequencies k ranging from 0.006 to 0.125). All the experiments were conducted at a fixed chord-based Reynolds number of 2.85 × 105. The choice of operating parameters invokes the linear and nonlinear behavior of the wing. The linear unsteady measurements agreed with the analytical results. The unsteady pressure measurements at higher offset levels revealed the nonlinear aerodynamic phenomenon of dynamic stall. This confirms that a nonlinear and dynamic model is required to capture the salient characteristics of the lift force on a pitching wing.

We first comprehensively analyze determining the generating capacities of each type, then discuss the heat distribution across interconnected district-level heat energy systems (“the hubs”) containing both low-scale and centralized heat plants; discussion is presented from the standpoint of primary energy consumption and costs. The applied tool includes a flexible heat generation model and an accurate objective function. The set of constraints and the Jacobian matrix are configured manually. We apply the fixed-point iteration approach to solve an equation system. In order to evaluate the consequences of all possible scenarios, we analyze a novel district heating (DH) system with low-scale solar collectors and heat pumps against central combined heat-and-power (CHP) plant fueled with fossil fuels and biomass. The proposed system is evaluated from the standpoint of the state-of-the-art, future prospects, and environmental impact. For each specific type of capacities, we herein present maximum achievable net generation of heat. System layout sketch is based on the data on the actual districts of Canberra, Australia. All the hubs are studied as a whole with due account of heat redistribution. The configuration made implies construction renewable energy facilities with a heat generation of 6.5+ MW, which will suffice to cover about a fifth of the peak load. This approach is quite novel for Australia. With greater electricity and heat supplies, renewable resources can cover an even greater share of such supplies. To sum up, a significant difference between the costs of a natural gas-based DHS and a coal-fired DH one lies in the fixed costs of operating the CHPPs and in the fuel costs. The self-generation of a hub may be inconsistent with the total energy balance, which is the total of eigen-production, energy inflows and outflows. The total system performance is considerably better when coal-fired CHPPs account for the bulk of net production.

The increasing sophistication of wind turbine design and control generates a need for high-quality wind data. The relatively limited set of available measured wind data may be extended with computer generated data, for example, to make reliable statistical studies of energy production and mechanical loads. Here, a data-driven model for the generation of surrogate wind speeds is compared with two state-of-the-art time series models that can capture the probability distribution and the autocorrelation of the target wind data. The proposed model, based on the phase-randomised Fourier transform, can generate wind speed time series that possess the power spectral density of the target data and converge to their generally non-Gaussian probability distribution with an arbitrary, user-defined precision. The model performance is benchmarked in terms of probability distribution, power spectral density, autocorrelation, and nonstationarities such as the diurnal and seasonal variations of the target data. Comparisons show that the proposed model can outperform the selected models in reproducing the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed. This work was partly supported by the Research Foundation Flanders (FWO) [grant number 74213/K231719N].

The present paper investigates the effectiveness of artificial neural networks for the identification of nonlinear state-space models in fluid dynamics. The test case under consideration consists in the modeling of the unsteady lift force of a pitching NACA 0018 airfoil at a Reynolds number Re = 3 x 10^5. Data used for model training and validation are obtained through Computational Fluid Dynamics (CFD) simulations performed in OpenFOAM. The State-Space Neural Network (SS-NN) model is trained using swept sines performed at several angle-of-attack ranges, and it is then validated using different sine sweeps as well as simple harmonic motions. The study demonstrates that the selected SS-NN model represents a powerful tool to accurately predict the unsteady aerodynamic loads of a pitching airfoil, both in pre-stall and post-stall conditions. In particular, the model succeeds in correctly capturing highly nonlinear flow features such as the delay in flow separation, and the formation and shedding of the dynamic stall vortex. The accuracy and evaluation speed of this technique make it particularly valuable for engineering applications involving design optimization and real-time control of systems based on lift. The obtained results represent a stepping-stone towards the integration of flexible, data-driven SS-NN models representing unsteady aerodynamics inside dynamic multi-physics models.

In many engineering applications, aerofoils experience elevated free-stream turbulence levels. The present work experimentally investigates the effect of free-stream turbulence on the aerodynamic characteristics of a sinusoidally pitching NACA 0018 aerofoil at the transitional Reynolds number of 2.8×105. Wind tunnel tests are conducted in quasi-static and dynamic conditions at different turbulence intensities between 0.3% and 8.2%, considering reduced frequencies up to 0.1. The dynamic experiments investigate multiple angle-of-attack ranges in order to quantify the influence of free-stream turbulence in the attached-flow regime, in the stalled regime, and in-and-out of stall. The study demonstrates that high free-stream turbulence drastically changes the flow physics over the aerofoil for both static and dynamic conditions, producing large deviations in the lift and moment coefficients. The quasi-static experiment performed at low free-stream turbulence features a large stall hysteresis linked to the breakdown of the leading-edge laminar separation bubble, whereas static hysteresis is not present at higher turbulence levels. Moreover, dynamic experiments at high angles of attack show a strong dependency on the incoming turbulence intensity, which is found to delay flow separation during the upstroke and enhance reattachment during the downstroke. The work also reveals the intrinsic difficulty of predicting the dynamic stall behaviour under different turbulence conditions, and gives insight into why the existing empirical dynamic stall models are unlikely to succeed in accurately predicting the aerodynamic loads across different onset turbulence intensities.

Modal analysis has developed into a major technology for the study of structural dynamics in the past several decades. Through it, complex structural dynamics phenomena can be represented in terms of structural invariants, i.e., the modal parameters: natural frequencies, damping ratios and mode shapes. Operational Modal Analysis (OMA) deals with the estimation of modal parameters on vibration data measured for operational conditions, when the excitation on the structure is not measured. In this work, OMA isperformed on a wind turbine blade undergoing wind tunnel testing. These tests included different wind speeds, pitch angles and also different health conditions of the blade,where masses of different magnitude were fixed to the blade on different locations to emulate damage conditions. In order to monitor the modal parameters across multiple days of varied tests in the wind tunnel, the Polymax modal parameter estimator was implemented, coupled with an Automated Modal Analysis methodology. This methodology included an automatic modal parameter selection technique, using a Machine Learning (ML) clustering algorithm, coupled with a modal tracking procedure which applied statistical thresholds on the modal parameters’ values. The tracking procedure searches for modes similar to the ones calculated for healthy conditions. The results show how the modal parameters of the wind turbine blade vary with the different measured conditions in the wind tunnel. Moreover, a damage detection methodology is implemented to differentiate between the healthy and damaged conditions on the blade, by leveraging an anomaly detection algorithm using the Multivariate Gaussian Distribution (MGD). This algorithm takes as input the modal parameters calculated by the previous Automated Modal Analysis methodology and detects statistical deviations among them which could indicate the presence of damage. All steps of this work contribute to developing an automatic framework able to detect damages on a wind turbine blade, and therefore perform Structural Health Monitoring (SHM) for different operational conditions.

Peritoneal metastasis is an advanced cancer type which can be treated with pressurized intraperitoneal aerosol chemotherapy (PIPAC). Here, chemotherapeutics are nebulized under high pressure in the intraperitoneal (IP) cavity to obtain a better biodistribution and tumor penetration. To prevent the fast leakage of chemotherapeutics from the IP cavity, however, nebulization of controlled release formulations is of interest. In this study, the potential of the thermosensitive hydrogel Pluronic F127 to be applied by high pressure nebulization is evaluated. Therefore, aerosol formation is experimentally examined by laser diffraction and theoretically simulated by computational fluid dynamics (CFD) modelling. Furthermore, Pluronic F127 hydrogels are subjected to rheological characterization after which the release of fluorescent model nanoparticles from the hydrogels is determined. A delicate equilibrium is observed between controlled release properties and suitability for aerosolization, where denser hydrogels (20% and 25% w/v Pluronic F127) are able to sustain nanoparticle release up to 30 hours, but cannot effectively be nebulized and vice versa. This is demonstrated by a growing aerosol droplet size and exponentially decreasing aerosol cone angle when Pluronic F127 concentration and viscosity increase. Novel nozzle designs or alternative controlled release formulations could move intraperitoneal drug delivery by high pressure nebulization forward.

How to estimate the exact value of pressure losses within a district heating (DH) network been in service for decades, which a pump at pumping station should compensate to achieve maximum benefits and the lowest costs relative to the scenario of the started process of transition to 4th generation district heating (4GDH)? Geographic Information System (GIS) software Zulu

The aim is to examine integrating a heat pump (HP) into a district heating (DH) system versus potential savings in the separate scenario. It takes into account all the properties of both DH system and a HP operation, and gives the understanding of temperature, pressure and enthalpy of refrigerant at each cycle point. Contribution to the pool of knowledge is considering a novel configuration, which allows to get rid of a heat exchanger between the water-source HP and the network. Scenarios of (i) separate operation of a HP, and (ii) integrating it into a DH system and inputting design data only, or (iii) utilizing operational data have been analyzed. Multiple thermal simulations have been performed. We also allow traditional coverage of heat demand with the help of a supply line of high-temperature DH (HTDH), thus elaborated what happens when the outdoor air temperature drops below lower threshold. Hence, the model identifies two stages of operating a HP integrated into a DH system. Starting from it, the temperature of the supply water should be 50 °C and above, so it is more effective to use supply water directly until the outdoor temperature rises again. This outdoor temperature implies change of operational mode, which leads to the following results. On cold winter days, the available amount of heat is lower than the energy consumption, therefore, energy from a supply line of HTDH is utilized, which gives overall energy saving effect of about 14 %. Mass flow rates through the configuration suggested are identical during moderate-cold fall and spring days, since the control logic is established with the help of variation of supply temperature only. Single HP has the lowest efficiency because of high temperatures needed: with a nominal temperature difference of 25 °C, the coefficient of performance (COP) rises to 5.0 only. At the break-even point, HP in each configuration has COP ranging between 3.51 and 4.43, while the rest of the time COP is just above 2. In different cases, the lowest threshold of HP's performance is 2.4, 2.6, and 2.89, respectively. The DH-assisted scenario, where peak energy demand was covered by DH system directly, clearly outperformed separate installation, where a HP was the only thing responsible to provide the necessary supply temperatures. For this case, lower COP stipulates less energy is supplied from the DH system, and therefore a lower share of consumption is supplied directly from supply line, which decreases the overall seasonal COP. Scenario (iii) of inputting DH operational data has the highest seasonal COPs, reasoned to higher than design return temperatures. Given the low performance of a HP raising water temperature to 55 °C (the lowest threshold acceptable in winter) a single HP with no backup capacity accounts for 23 % more energy use. Supported by a coal-fired combined heat-and-power plant, distribution of primary energy consumption is more favorable, i.e. coal-fired generation covers the peak. The positive effect is expanded by converting a part of heat demand from HP operation to cheaper HTDH operation.

Peritoneal metastasis (PM) is a type of cancer spread in which the peritoneum is affected by numerous tumor nodules. Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is an innovative drug delivery method developed to treat PM using laparoscopy. During PIPAC treatment, chemotherapeutics are nebulized as aerosol droplets using a high-pressure injector (Injektron™ 82 M, Medtron, Germany) and an atomizer (Capnopen®, Germany) with the aim to obtain a homogeneous droplet distribution throughout the peritoneal cavity, thereby increasing the effectiveness of intraperitoneal chemotherapy. The goal of the present work was to investigate the effect of additional electrostatic precipitation (ePIPAC) on the aerosol distribution during PIPAC in a realistically reconstructed human peritoneal cavity using computational fluid dynamics (CFD) modeling.

Research focus (problem description, short background): The energy efficiency of a district heating (DH) system can be improved by fine-tuning supply and return temperatures and flow rates without need for invasive renovation of the end user heating systems. Research methods: We employ a correlation coefficient to examine the relationship between the local supply temperature and the use of heat; research relies on data hourly heat generation records from each heat generation facility in the DH system of Omsk, Russia, 2016–2017. Key research results/findings: The correlation declines the further we go from winter. Firstly, the relationship between the transition states and the outside temperature results from the relationship between external conditionings and heat parameters in DH. Secondly, both the energy consumption and the supply temperature are affected by the infrastructure, other weather conditions, and energy price; they are also highly dependent on consumer behavior, which is arbitrary, heterogeneous, and interdependent. Thirdly, the regional heating load is influenced by the characteristics of a building cluster. Although nearly all DH plants have a control system in place, we find that human factor jeopardizes the performance of such systems. Main conclusions and recommendations: To generalize these results to other systems, we emphasize that the effective diffusion coefficients and effective heat-loss coefficients are influenced by the thermal dynamics in the network and change on typical time scale (one year), hence their uncertainty. This study has produced a technique for visualizing the relationship that exists in a DH system between supply temperature and heat consumption; this technique is expected to contribute to the dynamic modeling, control, and planning of future integrated energy systems. The load profiles are dynamic; a thermal storage could be used to shift the timing of heating loads to minimize the flow through DH networks.

Paper’s objective is to save time for district heating (DH) companies, which are new to the Geographic Information Systems-supported (GIS) modelling and optimizing heat consumption profiles; also to suggest specific guidelines for the system operation within a residential area. A specific case of the real DH network was simulated. A two-stage programming model combining the optimal design and operation of a DH pipe network was developed. We designed an aerial image of the testing area (modified from Google Map by means of QGIS) and to forecast the building thermal energy consumption, we used a pre-collected data set representing the heating season in Omsk, Russia. Weekday and weekend day were taken as the periods for forecasting. Actual profiles and the simulated data were compared. The error differs slightly for both forecasts regardless of a weekday or a weekend day it is. The obtained results are important because the simulation allows to cut off the peaks at 2:00 and 7:00. The error increases when the optimized values differ greater from the heat consumption basic profiles and vice versa. There are clearly visible local peaks induced by the domestic hot water (DHW) use and a manual temperature setting through a weekday. This leads to a larger inconsistency in relation to a weekend day. It is proposed to optimize the system operation, its objective is to minimize the daily heat energy consumption, annual total fuel demand consumption and the annualized cost of the network.

Purpose: This paper provides a novel method to split up the multiple coherence function into noise, nonlinear distortion, and transient components. Method: The method relies on the nonparametric estimation framework called the Best Linear Approximation (BLA) where vibro-acoustic systems are excited by special so-called multisines (pseudo-random noise) signals. Challange: Testing of multiple-input, multiple-output (MIMO) nonlinear systems is very involved, and it usually requires expert users. It is because the design and processing of experiments, and nonlinear assessment are not trivial questions. Proposal: To cope with these issues, this paper shows the recent results of a research project addressing the questions related to the user-friendly nonlinear (pre-)processing of MIMO experiments of vibrating structures. The key idea is the novel analysis of the (co-)variance estimates of the BLA framework that can be tailored to split up the classical multiple coherence function into noise, nonlinearity, and transient components. Conclusions: Using the suggested approach, a novice user can quickly determine whether the underlying structure is linear or not, and how much is the possible gain using nonlinear modeling tools. Illustration: The proposed approach is demonstrated on, but not limited to, the analysis of ground vibration testing of a decommissioned F-16 fighting falcon.

Accurate unsteady aerodynamic models are essential to estimate the forces on rapidly pitching wings and to develop model-based controllers. As system identification is arguably the most successful framework for model predictive control in general, in this paper we investigate whether system identification can be used to build data-driven models of pitching wings. The forces acting on the pitching wing can be considered a nonlinear dynamic function of the pitching angle and therefore require a nonlinear dynamic model. In this work, a nonlinear data-driven model is developed for a pitching wing. The proposed model structure is a polynomial nonlinear state-space model (PNLSS), which is an extension of the classical linear state-space model with nonlinear functions. The PNLSS model is trained on experimental data of a pitching wing. The experiments are performed using a dedicated wind tunnel setup. The pitch angle is considered as the input to the model, while the lift coefficient is considered as the output. Three models are trained on swept-sine signals at three offset angles with a fixed pitch amplitude and a range of reduced frequencies. The three training datasets are selected to cover the linear and nonlinear operating regimes of the pitching wing. The PNLSS models are validated on single-sine experimental data at the respective pitch offset angles. The PNLSS models are able to capture the nonlinear aerodynamic forces more accurately than a linear and semi-empirical models, especially at higher offset angles.

Understanding unsteady aerodynamic forces on a lifting surface is crucial for many engineering applications. This work demonstrates the use of system identification techniques for the data-driven modelling of the aerodynamic lift force on a pitching wing. This lift force can be considered as a (nonlinear) dynamic function of the angle of attack. The proposed approach consists of multiple steps: 1) excitation by the means of broadband excitation, 2) pre-processing of the measurement, and 3) data-driven modelling techniques. The considered excitation signal is the so-called multisine (also known as periodic pseudo- random noise). However, dynamic stall is known to exhibit important cluster-to-cluster variations and this is no different when utilizing multisines. Therefore, we cluster the data before the actual data-driven Preliminary proceedings modeling and validation. We show that the obtained models can capture the nonlinear aerodynamic forces more accurately than the classical linear and semi-empirical models.

This paper aims to improve the understanding of the wind turbine tower shadow effect by developing a model that combines potential flow and blade-element momentum theory. The change in the flow field due to the presence of the tower is discussed in detail, together with the resulting variations in the aerodynamic forces acting on the rotating blades, as well as the effect on the output power and the blade root bending moment. The tower shadow effect is not only due to the change in wind speed but also to the change in angle of attack. A one-dimensional description of the flow field is therefore bound to be inadequate. The impact of the tower diameter and blade-tower clearance on the rotor power and blade root bending moment can be expressed as a quadratic function of a non-dimensional parameter. Apart from a mild Reynolds-number effect, the results presented are independent of turbine size.

This paper describes the first results of the modelling of a plate heat exchanger which is installed in the VUB district heating network. A properly controlled energy exchange through the heat exchanger improves the economy of the entire system, minimizes the pollutant emissions and fossil fuel consumption, in accordance with the main goals of the European energy plan till 2030. To achieve these goals, an accurate model of the plate heat exchanger is required. In this article two approaches are considered to accurately model the heat exchanger: a physics-based white-box and a data-based black-box modelling technique. The white-box modelling approach uses the unsteady energy balance and heat transfer equations to predict the temperature evolutions in the plate heat exchanger. For the (data-driven) black-box modelling approach, there are three interrelated steps. Step 1 is the (semi-automatic) preprocessing of the measurements and the building of a nonparametric frequency response function (FRF) model. Step 2 is the building of a linear parametric (state-space) model, based on the FRF estimate. Step 3 is building a polynomial nonlinear state-space model that relies on the original linear state-space model. The proposed black-box method outperforms the white-box techniques in terms of prediction capabilities and computational needs.

In this paper, the performance of propellers is evaluated with 3D RANS and Vortex Theory for a wide range of geometries and advance ratios at high-altitude conditions. For this purpose, the results of a high-altitude propeller optimization are used as a CFD database and discussed briefly. A comparison is made between the predictions of 3D RANS and Vortex Theory with a goal to examine the agreement between the two models. Moreover, a variance-based sensitivity analysis is performed to determine geometrical or operational features of the propeller designs that influence the error between the two models. In the second part, the data from the two physical models are used to train different multi-fidelity surrogate models, using the data produced from 3D RANS, as the high-fidelity dataset and Vortex Theory, as the low-fidelity one. Different performance metrics are used to evaluate the predictive capabilities of the multi-fidelity and single-fidelity surrogate models in new propeller geometries. The predictions ofthese data-driven models are finally compared to the predictions of Vortex Theory.

As a result of the increased penetration of intermittent renewable energy sources, Combined Heat and Power (CHP) units are being looked upon as one of the sources that might provide for the ever growing need for electrical flexibility. However, CHP units are often considered as must-run units on the grid for their main purpose is generally to cover the heat demand of an adjoining District Heating Network (DHN). This paper demonstrates how a CHP–DHN system may be used as a frequency reserve without excessively compromising the lifetime of the CHP, using either a specific storage tank or the DHN's thermal inertia in order to compensate for the resulting heat imbalance. In the latter case, it is shown that a buffer tank, although smaller than a specific Thermal Energy Storage (TES), is required due to restrictions for acceptable DHN temperature gradients. In both approaches, the size of the tank has been mapped out considering frequency reserve's duration and capacity. The results show that a simplistic static model of the DHN is sufficient for the design of a specific TES, while a detailed dynamic simulation is required when the DHN is used as storage, to prevent overestimating the flexibility of the CHP–DHN system. This research could be used to assess the potential for improving CHP–DHN systems flexibility, using them as frequency reserves, and to design the required storage or buffer tanks.

The growing share of renewable energies in our electricity production, together with the still lacking storage capacity, strongly reinforces the need for more flexible electricity production units. In this context, combined cycle gas turbines (CCGTs) have a role to play, both in the current and future electricity production system due to their high efficiency, high load flexibility, and low CO2 emissions compared to other conventional thermal power plants. Nevertheless, bearing in mind our current challenges concerning climate change, the CO2 emissions of these CCGTs need to be reduced drastically. The amine-based absorption carbon capture (CC) process is currently the most mature and applicable CC technology. This process is known to require a considerable amount of thermal energy, degrading plant performance. However, to back-up renewable production, CCGTs will operate most of the time under part-load conditions. The impact of these part-load operations on the CC is still relatively unknown. Within this framework, this study aims to assess the performance of the CC process applied to a typical CCGT under part-load operation using specific simulation models. The CC plant model has been successfully validated against experimental data from a pilot-scale capture facility. Then, the CC plant has been scaled-up to the CCGT scale and the process has been optimized for each operating condition. The simulation results show that the specific reboiler duty increases for part-load operation, while the specific cooling requirements decrease. Moreover, the analysis of the yearly CCGT operation highlights a relative increase in CC energy penalty of 21% for an annual CCGT load factor of 0.5, impacting significantly plant performance. The next step will involve reducing this energy penalty.

The focus of this study is to investigate under high turbulence intensity the performance and wake characteristics of a pair of closely spaced counter-rotating vertical axis wind turbines (VAWTs) in wind tunnel-controlled conditions. To achieve this, wind tunnel experiments for an isolated VAWT and dual VAWTs are conducted. A squared-mesh wooden grid is placed upstream of the wind turbines to increase the level of free stream turbulence. The wind tunnel tests confirm the increase in power coefficient of paired VAWTs in turbulent flows compared to smooth flows. Additionally, this study analyses the near-wake downstream of the paired turbines in both smooth and turbulent flows to better understand the flow physics.

Waste heat recovery through cycle humidification is considered as an effective tool to increase the operational flexibility of micro gas turbines (mGTs) in cogeneration in a decentralized energy system (DES) context. Indeed, during periods with low heat demand, the excess thermal power can be reintroduced in the cycle under the form of heated water/steam, leading to improved electrical performance. The micro humid air turbine (mHAT) has been proven to be the most effective route for cycle humidification; however, so far, all research efforts focused on optimizing the mHAT performance at nominal electrical load, in the absence of any thermal load. Nevertheless, in a DES context, the thermal and electrical load of the mGT need to be changed depending on the demand, requiring both optimal nominal and part load performances. To address this need, in this paper, we present the first step toward the development of a control strategy for a Turbec T100 mGT-mHAT test rig. First, using experimental data, the global performance, depending on the operating point as well as the humidity level, has been assessed. Second, the performance of the saturation tower, i.e., the degree of saturation (relative humidity) of the working fluid leaving this saturator, is analyzed to assess the optimal water injection system control parameter settings. Results show that optimal mHAT performance can only be obtained when the working fluid leaving the saturation tower is fully saturated, but does not contain a remaining liquid fraction. Under these conditions, a maximal amount of waste heat is transferred from the water to the mGT working fluid in the saturation tower. From these data, some general observations can be made to optimize the performance, being maximizing injection pressure and aiming for a water flow rate of ≈5m3/h. Besides these general recommendations, having a specific control matrix, that allows setting the saturation tower control parameters for any set of operational setpoint and the inlet conditions would lead to optimized performance. Therefore, future work involves the development of a control matrix, using advanced data postprocessing for noise reduction and accuracy improvement, as well as an experimental validation of this methodology on the actual test rig.

This paper introduces a user-friendly estimation toolbox for (industrial) measurements of (vibro-acoustic) systems with multiple inputs. The vibration testing methods are very important because they help to improve the product quality and to avoid safety and comfort issues. The time-consuming testing procedures are nowadays fully substituted by techniques that evaluates the frequency response functions (FRFs). The toolbox provides a user-friendly nonparametric estimation method that uses a special combined Local Polynomial/Rational approach. The toolbox can be used even in case of multiple inputs with only one measured period — that would be not possible with the classical FRF estimator frameworks.

This paper provides the analytical solution to a one-parameter Šesták–Berggren kinetic model for the thermoanalytical study of pyrolysis reactions involving multiple independent parallel reactions. Such multiple independent parallel reactions are widely used, for instance, in the modeling of the pyrolysis of thermal protection materials used in heatshields for spacecraft during the atmospheric entry phase. Solving inverse problems to infer parameters of the kinetic model through optimization techniques or Bayesian inference methods for uncertainty quantification may require a large number of evaluations of the response and its sensitivities (derivatives with respect to parameters). Moreover, in the case of kinetic equations, the Arrhenius parameters can exhibit strong dependence that can require further model evaluations for an accurate parameter calibration. The interest of this analytical solution is to reduce computation cost while having high accuracy to perform parameter calibration from experiments and sensitivity analysis. We propose to use exponential–integral functions to express the solution of the temperature integral, and we derive the analytical solution and its sensitivities for the parallel reaction model both for constant temperature (isothermal) and for constant heating rate conditions. The solution is validated on a six-equation model using parameters inferred in a previous work from the experimental data of the pyrolysis of a phenolic-impregnated carbon ablator material, and we compare the computational cost and accuracy of the implemented analytical solution with a numerical solution. Our results show that the use of such analytical solution with an accurate computation of the exponential–integral function significantly reduces the computational cost compared to the numerical solution.

Temperature difference between supply and return distribution medium (water) is a vital factor when assessing the efficiency of a district heating (DH) substation. An accounting for fluctuations and differences of the heat consumption/generation is the key problem in planning DH system operation. The influence of the fluctuating energy consumption on a DH system was studied with actual data, using the DH systems of the Russian cities Krasnoyarsk and Omsk as a case study. Information is visualized in the form of graphs and charts, orderly and clearly comparing certain points. The data includes supply and return temperatures, and heat demand. Clearly visible state of high return temperatures induces more bottleneck problems as the flow increases. At the same time, in 2019, the total heat demand was 21 008 MW. This is more than 5 % than in 2020, assuming 100 % of consumers connected. The reasons for this trend are: decreasing total housing area, no incentive for the buildings in newly built-up areas to be connected to the DH system, poor service motivating business facilities to disconnect from the system. When the primary energy consumption related to the warmer climate and behaviour of business sector decreases, the DH system requires renovation. It is possible to reduce network return temperature during some months of the year. The reason is that, a high temperature difference is essential to maintain high efficiency and minimize fuel and pumping cost, it also enables more customers to be connected to a DH system without increasing pipe dimensions of a network.

The ignition of methane/air and ethylene/air mixtures by nanosecond pulsed discharges (NSPD) is investigated numerically using a zero-dimensional isochoric adiabatic reactor. A combustion kinetics model is coupled with a non-equilibrium plasma mechanism, which features vibrational and electronic excitation, dissociation, and ionization of neutral particles (O2 and N2) via electron impact. A time to ignition metric τ is defined, and ignition simulations encompassing a wide range of pressures (0.5–30 atm) and pulsing conditions for each fuel are executed. For each fuel, it is found that τ depends primarily on initial pressure and energy deposition rate, and scaling laws are derived. In order to quantify the benefit gained from plasma-assisted ignition (PAI), τ is compared with a thermal ignition time. It is found that for both fuels, PAI leads to a faster ignition at low pressures, while at higher pressures (p0 ≥ 5 atm), methane/air ignition becomes inefficient (meaning a longer ignition time for the same input energy compared to thermal ignition). Ethylene/air PAI shows only a modest deterioration. The drop in performance with pressure is found to be due to the mean electron energy achieved during the pulse, which shows an inverse relationship with pressure, leading to fewer excited species and combustion radicals. The poor performance of methane/air mixture ignition at high pressure is explained by an analysis of the reaction pathways. At high pressures (p0 ∼ 30 atm), H is consumed mostly to form hydroperoxyl (HO2), leading to a bottleneck in the formation of formyl (HCO) from formaldehyde (CH2O). Instead, for ethylene/air ignition, at both low and high pressures there exist several bypass pathways that facilitate the formation of HCO and CO directly from various intermediates, explaining the more robust performance of PAI for ethylene at pressure.

Black-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the function are tuned given the data. This is a pragmatic and often necessary step considering the black-box nature of the problem. However, having identified a suitable function, there is no need to stick to the original basis. So-called decoupling techniques aim at translating multivariate functions into an alternative basis, thereby both reducing the number of parameters and retrieving underlying structure. In this work a filtered canonical polyadic decomposition (CPD) is introduced. It is a non-parametric method which is able to retrieve decoupled functions even when facing non-unique decompositions. Tackling this obstacle paves the way for a large number of modelling applications., Accepted for presentation at the 19th IFAC Symposium on System Identification (SYSID 2021)

In this work recurrent neural networks of the LSTM type are used to describe unsteady fluid loading. Indirect load measurements are especially useful for structural health monitoring applications. Relying on discrete spatiotemporal measurements of the velocity field, both the forces and the corresponding displacement of a cylinder subjected to vortex shedding are modelled.

Intraperitoneal (IP) aerosolized anticancer drug delivery was recently introduced in the treatment of patients with peritoneal metastases. However, little is known on the effect of treatment parameters on the spatial distribution of the aerosol droplets in the peritoneal cavity. Here, computational fluid dynamics (CFD) modeling was used in conjunction with experimental validation in order to investigate the effect of droplet size, liquid flow rate and viscosity, and the addition of an electrostatic field on the homogeneity of IP aerosol. We found that spatial distribution is optimal with small droplet sizes (1–5 µm). Using the current clinically used technology (droplet size of 30 µm), the optimal spatial distribution of aerosol is obtained with a liquid flow rate of 0.6 mL s−1. Compared to saline, nebulization of higher viscosity liquids results in less homogeneous aerosol distribution. The addition of electrostatic precipitation significantly improves homogeneity of aerosol distribution, but no further improvement is obtained with voltages higher than 6.5 kV. The results of the current study will allow to choose treatment parameters and settings in order to optimize spatial distribution of IP aerosolized drug, with a potential to enhance its anticancer effect.

Increasing the share of Renewable energy sources in District Heating (DH) systems is of great importance to mitigate their CO2 emissions. The combined integration of Solar Thermal Collectors (STC) and Thermal Energy Storage (TES) into existing Combined Heat and Power (CHP) systems can be a very cost-effective way to do so. This paper aims at finding the optimal design of STC and TES systems integrated in existing CHP’s considering two distinct objectives: economic profitability and environmental impact. To do so, we developed a three-stage framework based on Pareto-optimal solutions generated by multi-objective optimization, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-entropy method to select the optimal solution, followed by the definition of final Operation strategy. We proposed relevant improvement of the state-of-the-art models used in similar analysis. We also applied the proposed methodology to the case of a representative, 12 MWth CHP plant. Our results show that, while the addition of TES or STC alone results in limited performances and/or higher costs, both the cost and the CO2 emissions can be reduced by integrating the optimal combination of STC and TES. For the selected, optimal solution, carbon emissions are reduced by 10%, while the Annual Total Cost (ATC) is reduced by 3%. It also improves the operational flexibility and the efficiency by peak load shaving, load valley filling and thus by decreasing the peak load boiler operation. Compared to the addition of STC alone, the use of TES results in an increased efficiency, from 88% to 92%. The optimal share of STC is then increased from 7% to 10%.

This paper describes the design and optimization of the model-based predictive control (MPC) of a substation in the district heating system of the VUB campus. A properly controlled energy exchange improves the economy of the entire system while minimizing the pollutant emissions and fossil fuel consumption; the main goal of the European energy plan until 2030. The designed MPC modifies the flow rates in the substation, based on temperature evolutions and continuously provides an optimal return temperature in the network, while dispatching the desired amount of thermal power to the building. Compared to the installed proportional integral (PI) control, a reduction in substation return temperature of 10 °C can be obtained. The environmental and economic benefits of this reduced substation return temperature were assessed via a techno-economical model of the condensing boiler in the DHS, showing a 13% increase in overall efficiency, 11% of operational savings and a reduction of 5 ton in CO2 emissions, when compared with the experimental data of December 2020.

Scientists and engineers want accurate mathematical models of physical systems for understanding, design, and control. To obtain accurate models, persistently exciting rich signals are needed. The MUMI Matlab toolbox creates multisine signals to assess the underlying systems in a time efficient, user-friendly way. In order to avoid any spectral leakage, to reach full nonparametric characterization of the noise, and to be able to detect nonlinearities and time-variations, multisine signals can be used. By the use of the toolbox, inexperienced users can easily create state-of-the-art excitation signals to be used to perturbate almost any physical systems.

In this paper, we investigate the correlation between the main physical characteristics of eight variants of football and hockey (such as field size, goal size, player velocity, ball velocity, player density, and game duration) and the resulting average numbers of goals scored per game. To do so, the Pi-theorem in physics is extended to sport science and a non-dimensional parameter of interest is defined. It is based on the ratio between the duration of the game and the order of magnitude of the time needed to cross the midfield, which depends on the average velocity of the ball and the players, the player density and the size of the goals. An excellent correlation is found between the proposed parameter and the average number of goals scored per game during recent international competitions. Using the derived correlation, the effect of any modification of the main characteristics of football and hockey (and their variants) on the scoring pace can be assessed. For instance, it can be predicted that decreasing the length of football fields by 20 m would raise the average number of goals scored to 3.6 (±0.6) per game, versus the 2.6 goals scored during the most recent men’s World Cup.

As the structural limits for the conventional horizontal-axis wind turbine are being reached, innovative concepts are needed to ensure increased reliability and reduced costs. One option is limiting the fatigue to which a rotor is subjected. This study, therefore, intends to experimentally investigate the tower shadow effect for a deeper understanding of the aerodynamic fluctuations, and to study the capability of a lower surface microtab to mitigate such fluctuations. For this, aerodynamic load variations caused by the presence of the tower on the rotor blades were reproduced and studied in the wind tunnel using a setup that mimics the interaction between the tower and the blade. Anemometers and flow visualisation techniques were used to explore the influence of the tower on the surrounding flow field. Real-time measurements show that the alterations in the lift force are mainly caused by the alterations in the flow angle and have proven that a lower surface microtab can partially mitigate aerodynamic load variations in the case of an upwind rotor blade. Experimental results are validated using a tower shadow model that is based on potential flow theory. The tower shadow model shows a reduction in 3p power oscillations by a factor of 2.

To satisfy the increasing demand for energy and potable water, large-scale cogeneration is widely integrated. However, in remote locations, the lack of power system infrastructure limits the integration of large-scale systems. Consequently, a large portion of inhabitants has no access to electricity and the pressure on groundwater resources increases drastically. To address this power and water scarcity, a distributed cogeneration system consisting of a solar-powered micro Gas Turbine and desalination system is considered. Since the integration of solar energy in distributed cogeneration systems is uncertain, we performed a feasibility study. This paper covers the modelling, sensitivity and exergy analysis and 3 desalination systems designs, each making a trade-off between smaller plant size and higher performance. Introducing solar energy in the micro gas turbine results in an increase in electrical efficiency by 3.2% absolute. The proposed designs achieve a levelized cost of water between 1.78$/(m3/d) and 1.92$/(m3/d), which is comparable with conventional solar-powered desalina-tion plants. Therefore, these designs demonstrate the feasibility of integrating solar energy in distributed cogeneration systems and provide a promising solution towards cost-efficient, renewable-based power and water cogeneration in remote locations. The future work will enhance the economic analysis by including an intermittent solar energy supply.

Wind tunnel tests have been performed of individual and paired H-type Darrieus vertical-axis wind turbines. The turbines in the paired configuration are closely spaced, at 1.2 and 1.3 rotor diameters shaft to shaft, and are counter-rotating. Two directions of rotation were studied, one where the facing (inner) blades move along with the incoming flow, and one where the facing blades move against the wind. The wind tunnel tests confirm a net increase in the power coefficient of the paired configuration compared with twice the power coefficient of the individual turbine. We found average relative increases in the power coefficients between 13% and 16%, which is consistent with numerical studies available in the literature.

One of the fundamental considerations in space heating (SH) consists in the variability of heat loads over time as result of external conditions. This paper shows the novel way to adapt a variable-flow system provide space heating (SH) and domestic hot water (DHW) to multiple buildings with varying loads. It would be ideal to provide only as much heating and DHW as the zone actually requires at any particular moment. Constant-volume systems come close to this ideal. One needs to know both indoor and outdoor design temperatures, mixed-water temperature, and the required sensible and latent heat loads. Only through analyzing the real network behavior one can gain useful insights related to the consumption patterns of the users. The investigated district heating (DH) system of Zavertyaevo District, Omsk, Russia consists of pipework within a concrete surface trench covered with earth, and two gas boilers with a rated SH supply capacity of 13.3 MW and design efficiency of 82.6%. This system is considered in a case where the SH temperature equals 70 °C and the post-SH return temperature equals 55 °C. This part is related to an idea based on combining lower supply/return temperatures and local heat pump placement, which is in line with the abovementioned LTDH concept. When maximal water flow is reached, if the zone is still too cool, SH is partially provided by a thermostatically controlled electric heater or a heat pump. This means there may be some energy wasted in the constant-volume system due to simultaneous electric heating and drops in the supply temperature. However, this energy waste is far less significant than in variable-flow systems, since the supply temperature is reduced to a minimum before the warm weather starts. These control operations can be summarized in the following chart often referred to as control logic. Advantages of constant-volume systems include low capital costs and low maintenance costs, whereby the former become lower as the system can use the current pipeline dimensions and only requires a simple control at the plant and at the substations to be placed while renovated; the latter costs are lower because the constant volume of water, and thus the boiler capacity and pump power closely follow the actual building-specific heat consumption.

High-fidelity thermodynamic simulation software is available to perform detailed simulations of power plants. However, these models depend on many operating parameters the user must characterize to assess the power plant performances. Unfortunately, most parameters are underdetermined by experience: the validation of the model using field measurements does not allow for the complete determination of all parameters, notwithstanding the unavoidable uncertainties of the measurements themselves. These limitations can result in a drastic mismatch between simulated and actual performance and lead to biased, suboptimal decision-making. To address these limitations, we performed Uncertainty Quantification on key furnace heat transfer parameters to predict the thermodynamic performance of coal-fired power plants retrofitted to biomass (co-)firing under uncertain operating conditions. We used a high-fidelity Thermoflex® model to simulate the thermodynamic performance of the power plant, and we adopted Polynomial Chaos Expansion to perform Uncertainty Quantification in a computationally-efficient manner. Finally, we evaluated the effect of various fractions of biomass in the fuel (from 0 to 100%) on the performance, which provides additional information in the decision-making process during the retrofit of the power plant. The results illustrate that the uncertainty on the non-uniform radiant flux factor dominates the uncertainty on the power, efficiency and flue gas temperature, meaning that efforts should aim at reducing the epistemic uncertainty on the radiative heat flux in the boiler. Increasing the biomass fraction results in a decrease in the gross power and gross efficiency. The mean Furnace Exit Gas Temperature remains relatively stable, but reaches a minimum value at 60% biomass co-firing. In conclusion, Polynomial Chaos Expansion allows for a computationally-efficient probabilistic assessment of non-validated operational conditions, such as a fuel switch, in high-fidelity models for thermal power plants. Future work will focus on extending the number of uncertain parameters.

This paper aims at assessing the impact of retrofitting an existing, 730 MWe, coal-fired power plant into a biomass-fired combined heat and power (CHP) plant on its energetic and exergetic performances. A comprehensive thermodynamic model of the power plant was developed and validated against field data, resulting in less than 1% deviation between the model and the measurements for the main process parameters. The validated model was then used to predict the behaviour of the biomass CHP after retrofitting. The modelled CHP unit is coupled to a steam-explosion biomass upgrading plant, a biorefinery process, and a high-temperature heat network. 13 scenarios were studied. At constant boiler load, delivering heat to the considered heat clients can increase the total energy efficiency of the plant from 44% (electricity only) to 64%, while the total exergy efficiency decreases from 39% to 35%. A total energy efficiency of 67% could be reached by lowering the network temperature from 120∘C to 70∘C. Identifying the needed heat clients could, however, represent a limiting factor to reach such high efficiencies. For a constant power demand, increasing the boiler load from 80 to 100% in order to provide additional heat makes the total energy efficiency increase from 43% to 55%, while the total exergy efficiency decreases from 39% to 36%.

Ammonia and hydrogen are promoted as potential energy carriers for centralized energy restitution. This article investigates ammonia/hydrogen/air premixed turbulent combustion, using Large-Eddy Simulations, in an academic atmospheric gas turbine swirled burner. A one-dimensional flame analysis demonstrates the existence of a trade-off in NOX and NH3 emissions for ammonia/hydrogen blends, and the possibility to obtain 1D flame propagation characteristics close to that of a lean methane flame by adjusting the amount of H2. Large-Eddy Simulations of the PRECCINSTA burner exhibit stable combustion, while the optimized trade-off equivalence ratio is pinpointed at φ=1.46 for XH2Fuel=0.46. Corresponding emissions are XNOX≈XNH3≈300 ppmv. Large amounts of hydrogen are found in the exhaust gases, inducing a low combustion efficiency. The flame structure, combustion dynamics, influence of kinetics modelling and mesh resolution are discussed. This work paves the way for future studies, in the perspective of applications to industrial systems., SCOPUS: ar.j, info:eu-repo/semantics/published

The control of a nonlinear system such as an underground coal gasification (UCG) process is a challenging task. Several nonlinear design approaches are implemented to improve the tracking performance of the UCG process, however, the nonlinear techniques make implementation complex and computationally inefficient. In this work, a constrained linear model predictive control (MPC) is designed for the UCG process to track the desired trajectory of the heating value, while satisfying actuator constraints pertaining to UCG. The unknown states required for MPC design are reconstructed by using linear adaptive Kalman filter (AKF) and unscented Kalman filter (UKF). The design of MPC and AKF is based on the quasi-linear model of the UCG process. A fair comparison between different control strategies is conducted which include MPC– AKF, MPC– UKF, MPC– gain scheduled modified Utkin observer (GSMUO) and dynamic integral sliding mode control (DISMC)–GSMUO. The quantitative analysis and simulation results show that MPC- AKF outperforms its counterparts by yielding the least tracking error and average control energy. This conclusion holds, even in the presence of an external disturbance, parametric variations, and measurement and process noises. Moreover, MPC- AKF yields 51%, 44% and 46% improvement in absolute relative root-mean-squared error with reference to MPC– UKF, MPC– GSMUO and DISMC–GSMUO, respectively. A quantitative analysis has also been carried for AKF and UKF, which shows that the performance of AKF is more robust against changes in the initial values of measurement and process covariances.