Predictive modeling analysis of a wet cooling tower - Adjoint sensitivity analysis, uncertainty quantification, data assimilation, model calibration, best-estimate predictions with reduced uncertainties

It is common practice, in the modern era, to base the process of understanding and eventually predicting the behavior of complex physical systems upon simulating operational situations through system codes. In order to provide a more thorough and accurate comprehension of the system dynamics, these numerical simulations are often and preferably flanked by experimental measurements. In practice, repeated measurements of the same physical quantity produce values differing from each other and from the measured quantity true value, which remains unknown; the errors leading to this variation in results can be of methodological, instrumental or personal nature. It is not feasible to obtain experimental results devoid of uncertainty, and this means that a range of values possibly representative of the true value always exists around any value stemming from experimental measurements. A quantification of this range is critical to any practical application of the measured data, whose nominal measured values are insufficient for applications unless the quantitative uncertainties associated to the experimental data are also provided. Not even numerical models can reveal the true value of the investigated quantity, for two reasons: first, any numerical model is imperfect, meaning that it constitutes an inevitable simplification of the real world system it aims to represent; in second place, a hypothetically perfect model would still have uncertain values for its model parameters - such as initial conditions, boundary conditions and material properties - and the stemming results would therefore still be differing from the true value and from the experimental measurements of the quantity. With both computational and experimental results at hand, the final aim is to obtain a probabilistic description of possible future outcomes based on all recognized errors and uncertainties. This operation falls within the scope of predictive modeling procedures, which rely on three key elements: model calibration, model extrapolation and estimation of the validation domain. The first step of the procedure involves the adjustment of the numerical model parameters accordingly to the experimental results; this aim is achieved by integrating computed and measured data, and the associated procedure is known as model calibration. In order for this operation to be properly executed, all errors and uncertainties at any level of the modeling path leading to numerical results have to be identified and characterized, including errors and uncertainties on the model parameters, numerical discretization errors and possible incomplete knowledge of the physical process being modeled. Calibration of models is performed through the mathematical framework provided by data assimilation procedures; these procedures strongly rely on sensitivity analysis, and for this reason are often cumbersome in terms of computational load. Generally speaking, sensitivity analyses can be conducted with two different techniques, respectively known as direct or forward methods and adjoint methods. The forward methods calculate the finite difference of a small perturbation in a parameter by means of differences between two independent calculations, and are advantageous only for systems in which the number of responses exceeds the number of model parameters; unfortunately this is seldom the case in real large-scale systems. In this work, this problem has been overcome by using the adjoint sensitivity analysis methodology (ASAM) by Cacuci: as opposed to forward methods, the ASAM is most efficient for systems in which the number of parameters is greater than the number of responses, such as the model investigated in this thesis and many others currently used for numerical simulations of industrial systems. This methodology has been recently extended to second-order sensitivities (2nd-ASAM) by Cacuci for linear and nonlinear systems, for computing exactly and efficiently the second-order functional derivatives of system responses to the system model parameters. Model extrapolation addresses the prediction of uncertainty in new environments or conditions of interest, including both untested parts of the parameter space and higher levels of system complexity in the validation hierarchy. Estimation of the validation domain addresses the estimation of contours of constant uncertainty in the high-dimensional space that characterizes the application of interest. The present work focuses on performing sensitivity and uncertainty analysis, data assimilation, model calibration, model validation and best-estimate predictions with reduced uncertainties on a counter-flow, wet cooling tower model developed by Savannah River National Laboratory. A cooling tower generally discharges waste heat produced by an industrial plant to the external environment. The amount of thermal energy discharged into the environment can be determined by measurements of quantities representing the external conditions, such as outlet air temperature, outlet water temperature, and outlet air relative humidity, in conjunction with computational models that simulate numerically the cooling tower behavior. Variations in the model parameters (e.g., material properties, model correlations, boundary conditions) cause variations in the model response. The functional derivatives of the model response with respect to the model parameters (called “sensitivities”) are needed to quantify such response variations changes. In this work, the comprehensive adjoint sensitivity analysis methodology for nonlinear systems is applied to compute the cooling tower response sensitivities to all of its model parameters. Moreover, the utilization of the adjoint state functions allows the simultaneous computation of the sensitivities of each model response to all of the 47 model parameters just running a single adjoint model computation; obtaining the same results making use of finite-difference forward methods would have required 47 separate computations, with the relevant disadvantage of leading to approximate values of the sensitivities, as opposed to the exact ones yielded by applying the adjoint procedure. In addition, the forward cooling tower model presents nonlinearity in their state functions; the adjoint sensitivity model possess the relevant feature of being instead linear in the adjoint state functions, whose one-to-one correspondence to the forward state functions is essential for the calculation of the adjoint sensitivities. Sensitivities are subsequently used in this work to realize many operations, such as: (i) ranking the model parameters according to the magnitude of their contribution to response uncertainties; (ii) determine the propagation of uncertainties, in form of variances and covariances, of the parameters in the model in order to quantify the uncertainties of the model responses; (iii) allow predictive modeling operations, such as experimental data assimilation and model parameters calibration, with the aim to yield best-estimate predicted nominal values both for model parameters and responses, with correspondently reduced values for the predicted uncertainties associated. The methodologies are part of two distinct mathematical frameworks: the Adjoint Sensitivity Analysis Methodology (ASAM) is used to compute the adjoint sensitivities of the model quantities of interest (called “model responses”) with respect to the model parameters; the Predictive Modeling of Coupled Multi-Physics Systems (PM_CMPS) simultaneously combines all of the available computed information and experimentally measured data to yield optimal values of the system parameters and responses, while simultaneously reducing the corresponding uncertainties in parameters and responses. In the present work, a relevantly more efficient numerical method has been applied to the cooling tower model analyzed, leading to the accurate computation of the steady-state distributions for the following quantities of interest: (i) the water mass flow rates at the exit of each control volume along the height of the fill section of the cooling tower; (ii) the water temperatures at the exit of each control volume along the height of the fill section of the cooling tower; (iii) the air temperatures at the exit of each control volume along the height of the fill section of the cooling tower; (iv) the humidity ratios at the exit of each control volume along the height of the fill section of the cooling tower; and (v) the air mass flow rates at the exit of the cooling tower. The application of the numerical method selected eliminates any convergence issue, yielding accurate results for all the control volumes of the cooling tower and for all the data set of interest. This work is organized as follows: Chapter 2 provides a description of the physical system simulated, along with presenting the mathematical model used in this work for simulating a counter-flow cooling tower operating under saturated and unsaturated conditions. The three cases analyzed in this work and their corresponding sets of governing equations are detailed in this chapter. Chapter 3 presents the development of the adjoint sensitivity model for the counter-flow cooling tower operating under saturated and unsaturated conditions using the general adjoint sensitivity analysis methodology (ASAM) for nonlinear systems. Using a single adjoint computation enables the efficient and exact computation of the sensitivities (functional derivatives) of the model responses to all of the model parameters, thus alleviating the need for repeated forward model computations in conjunction with finite difference methods. The mathematical framework of the “predictive modeling for coupled multi-physics systems” (PM_CMPS) is also detailed. Chapter 4 presents the results of applying the ASAM and PM_CMPS methodologies to all the cases listed in Chapter 2: after being calculated, sensitivities are subsequently used for ranking the contributions of the single model parameters to the model responses variations, for computing the propagated uncertainties of the model responses, and for the application of the PM_CMPS methodology, aimed at yielding best-estimate predicted nominal values and uncertainties for model parameters and responses. This methodology simultaneously combines all of the available computed information and experimentally measured data for the counter-flow cooling tower operating under saturated and unsaturated conditions. The best-estimate results predicted by the PM_CMPS methodology reveal that the predicted values of the standard deviations for all the model responses, even those for which no experimental data have been recorded, are smaller than either the computed or the measured standards deviations for the respective responses. This work concludes with Chapter 5 by discussing the significance of these predicted results and by indicating possible further generalizations of the adjoint sensitivity analysis and PM_CMPS methodologies

A Software Suite for the Control and the Monitoring of Adaptive Robotic Ecologies

Adaptive robotic ecologies are networks of heterogeneous robotic devices (sensors, actuators, automated appliances) pervasively embedded in everyday environments, where they learn to cooperate towards the achievement of complex tasks. While their flexibility makes them an increasingly popular way to improve a system’s reliability, scalability, robustness and autonomy, their effective realisation demands integrated control and software solutions for the specification, integration and management of their highly heterogeneous and computational constrained components. In this extended abstract we briefly illustrate the characteristic requirements dictated by robotic ecologies, discuss our experience in developing adaptive robotic ecologies, and provide an overview of the specific solutions developed as part of the EU FP7 RUBICON Project

Is now the time for molecular driven therapy for diffuse large B-cell lymphoma?

INTRODUCTION: Recent genetic and molecular discoveries regarding alterations in diffuse large B-cell lymphoma (DLBCL) deeply changed the approach to this lymphoproliferative disorder. Novel additional predictors of outcomes and new therapeutic strategies are being introduced to improve outcomes. Areas covered: This review aims to analyse the recent molecular discoveries in DLBCL, the rationale of novel molecular driven treatments and their impact on DLBCL prognosis, especially in ABC-DLBCL and High Grade B Cell Lymphoma. Pre-clinical and clinical evidences are reviewed to critically evaluate the novel DLBCL management strategies. Expert commentary: New insights in DLBCL molecular characteristics should guide the therapeutic approach; the results of the current studies which are investigating safety and efficacy of novel 'X-RCHOP' will probably lead, in future, to a cell of origin (COO) based upfront therapy. Moreover, it is necessary to identify early patients with DLBCL who carried MYC, BCL2 and/or BCL6 rearrangements double hit lymphomas (DHL) because they should not receive standard R-CHOP but high intensity treatment as reported in many retrospective studies. New prospective trials are needed to investigate the more appropriate treatment of DHL

Relapsed/Refractory Diffuse Large B-Cell Lymphoma (R/R DLBCL) Patients: A Retrospective Analysis of Eligibility Criteria for CAR-T Cell Therapy

Patients (pts) with diffuse large B-cell lymphoma (DLBCL) refractory to second-line therapy or relapsed after an autologous stem cell transplant (ASCT) have a very poor clinical outcome with a median overall survival (OS) of 5 and 8-10 months, respectively. Autologous anti-CD19 chimeric antigen receptor (CD19 CAR) T cells have been associated with sustained complete remissions and long-term survivals in a large proportion of pts with R/R DLBCL by the two pivotal clinical trials Zuma1 and Juliet. This has led to the rapid approval by FDA and then by EMA of CAR-T cells for the third-line treatment of R/R DLBCL. Despite being a potentially revolutionary treatment for pts with advanced disease, the costs are much greater than any previously approved cancer therapy and this may become a substantial economic challenge for the health care system. The definition of inclusion and exclusion criteria capable of identifying more precisely pts who can successfully undergo CAR-T cell therapy, minimizing the severity of the toxicity, still remains a matter of discussion. Moreover, some eligible pts run the risk of becoming ineligible because of poor disease control. Indeed, one of the major obstacles to the successful use of CAR-T cells is the 4-5 week period so far required for the manufacturing and transfer of CAR-T cells. To address this issue, we have examined data of R/R DLBCL pts managed between 2010 and 2018 at our Center in order to: 1) better identify the characteristics and outcome of a cohort of R/R DLBCL pts potentially eligible, according to the approval criteria, for CAR-T cell therapy; 2) define factors influencing CAR-T cell eligibility; 3) make a realistic estimate of pts eligible for CAR-T cells. In this retrospective real-life cohort of R/R DLBCLs, 82/480 pts (17%) were R/R tosecond-line treatment including ASCT. Considering Juliet's inclusion/exclusion criteria for CAR-T cell therapy, only 50 pts (10.4%) would be eligible for CAR-T cells. Our analysis suggests that elevated LDH plus ECOG ≥2 have to be considered the two most significant features of very rapid disease progression. These variables should be taken in account in order to better select DLBCL pts potentially eligible to CAR-T therapy

Predictive Modeling of a Buoyancy-Operated Cooling Tower under Unsaturated Conditions: Adjoint Sensitivity Model and Optimal Best-Estimate Results with Reduced Predicted Uncertainties

Nuclear and other large-scale energy-producing plants must include systems that guarantee the safe discharge of residual heat from the industrial process into the atmosphere. This function is usually performed by one or several cooling towers. The amount of heat released by a cooling tower into the external environment can be quantified by using a numerical simulation model of the physical processes occurring in the respective tower, augmented by experimentally measured data that accounts for external conditions such as outlet air temperature, outlet water temperature, and outlet air relative humidity. The model’s responses of interest depend on many model parameters including correlations, boundary conditions, and material properties. Changes in these model parameters induce changes in the computed quantities of interest (called “model responses”), which are quantified by the sensitivities (i.e., functional derivatives) of the model responses with respect to the model parameters. These sensitivities are computed in this work by applying the general adjoint sensitivity analysis methodology (ASAM) for nonlinear systems. These sensitivities are subsequently used for: (i) Ranking the parameters in their importance to contributing to response uncertainties; (ii) Propagating the uncertainties (covariances) in these model parameters to quantify the uncertainties (covariances) in the model responses; (iii) Performing model validation and predictive modeling. The comprehensive predictive modeling methodology used in this work, which includes assimilation of experimental measurements and calibration of model parameters, is applied to the cooling tower model under unsaturated conditions. The predicted response uncertainties (standard deviations) thus obtained are smaller than both the computed and the measured standards deviations for the respective responses, even for responses where no experimental data were available

Hardware-in-the-Loop Platform for Assessing Battery State Estimators in Electric Vehicles

The development of new algorithms for the management and state estimation of lithiumion batteries requires their verification and performance assessment using different approaches and tools. This paper aims at presenting an advanced hardware in the loop platform which uses an accurate model of the battery to test the functionalities of battery management systems (BMSs) in electric vehicles. The developed platform sends the simulated battery data directly to the BMS under test via a communication link, ensuring the safety of the tests. As a case study, the platform has been used to test two promising battery state estimators, the Adaptive Mix Algorithm and the Dual Extended Kalman Filter, implemented on a field-programmable gate array based BMS. Results show the importance of the assessment of these algorithms under different load profiles and conditions of the battery, thus highlighting the capabilities of the proposed platform to simulate many different situations in which the estimators will work in the target application

System on chip battery state estimator: E-bike case study

This paper discusses the hardware implementation and experimental validation of a model-based battery state estimator. The model parameters are identified online using the moving window least squares method. The estimator is implemented in a field programmable gate array device as a hardware block, which interacts with the embedded processor to form a system on a chip battery management system (BMS). As a case study, the BMS is applied to the battery pack of an e-bike. Road tests show that the implemented estimator may provide very good performance in terms of maximum and rms estimation errors. This work also proposes a new methodology to assess the performance of a battery state estimator

EFFECTS OF FATIGUE ON KINEMATICS AND SHOCK ATTENUATION DURING DOWNHILL TRAIL RUNNING

This study assessed the effects of a competitive trail run on running kinematics and shock attenuation in well-trained trail runners. Nine male runners performed a simulated short trail running race. Prior and 5-min after the race, participants completed a 290-m downhill run at pre-determined preferred speed. Inertial measurement units were used to assessselected kinematic parameters. The contact time showed a moderate increase in the fatigued condition (pre: 0.215 (0.024) s vs. post: 0.226 (0.219) s; p2vs. post: 49.1 (11.9) m/s2; p=0.038; d=0.56), while peak tibial acceleration and shock attenuation showed no change (p\u3e0.05). These findings confirm that running-induced fatigue impacts running kinematics, although shock attenuation was unaltered with the present fatiguing protocol. This study assessed the effects of a competitive trail run on running kinematics and shock attenuation in well-trained trail runners. Nine male runners performed a simulated short trail running race. Prior and 5-min after the race, participants completed a 290-m downhill run at pre-determined preferred speed. Inertial measurement units were used to assessselected kinematic parameters. The contact time showed a moderate increase in the fatigued condition (pre: 0.215 (0.024) s vs. post: 0.226 (0.219) s; p2vs. post: 49.1 (11.9) m/s2; p=0.038; d=0.56), while peak tibial acceleration and shock attenuation showed no change (p\u3e0.05). These findings confirm that running-induced fatigue impacts running kinematics, although shock attenuation was unaltered with the present fatiguing protocol

Comparison of State and Parameter Estimators for Electric Vehicle Batteries

A Battery Management System (BMS) is needed to ensure a safe and effective operation of a Lithium-ion battery, especially in electric vehicle applications. An important function of a BMS is the reliable estimation of the battery state in a wide range of operating conditions. To this end, a BMS often uses an equivalent electrical model of the battery. Such a model is computationally affordable and can reproduce the battery behaviour in an accurate way, assuming that the model parameters are updated with the actual operating condition of the battery, namely its state-of-charge, temperature and ageing state. This paper compares the performance of two battery state and parameter estimation techniques, i.e., the Extended Kalman Filter and the classic Least Squares method in combination with the Mix algorithm. Compared to previous ones, this work focuses on the concurrent estimation of battery state and parameters using experimental data, measured on a Lithium-ion cell subject to a current profile significant for an electric vehicle application

Lifetime use of psychedelics is associated with better mental health indicators during the COVID-19 pandemic

Background and aimsThe COVID-19 pandemic and its consequences represent a major challenge to the mental health and well-being of the general population. Building on previous work on the potential long-term benefits of psychedelics, we hypothesized that lifetime use of these drugs could be linked to better mental health indicators in the context of the ongoing pandemic.MethodsTwo anonymous online surveys were conducted between April and June 2020, including questions about lifetime experience with psychedelics and other psychoactive drugs, and psychometric scales designed to measure personality traits, anxiety, negative, and positive affect, well-being, and resilience. Principal component analysis was applied to divide the sample into groups of subjects based on their drug use reports.ResultsFive thousand six hundred eighteen participants (29.15 ± 0.12 years, 71.97% female) completed both surveys and met the inclusion criteria, with 32.43% of the sample reporting at least one use of a psychedelic drug. Preliminary analyses showed that certain psychedelics were linked to improved mental health indicators, while other psychoactive drugs exhibited the opposite behavior. Lifetime psychedelic use was linked to increased openness and decreased conscientiousness, and to higher scores of positive affect. The reported number of past psychedelic experiences predicted higher scores of the secondary personality trait beta factor, which has been interpreted as a measure of plasticity. No significant associations between lifetime use of psychedelics and indicators of impaired mental health were observed.ConclusionWe did not find evidence of an association between lifetime use of psychedelics and poor mental health indicators. Conversely, experience with psychedelic drugs was linked to increased positive affect and to personality traits that favor resilience and stability in the light of the ongoing crisis.Fil: Cavanna, Federico Amadeo. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Pallavicini, Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Milano, Virginia. No especifíca;Fil: Cuiule, Juan. No especifíca;Fil: Di Tella, Rocco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: González, Pablo. No especifíca;Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad Adolfo Ibañez; Chil