Numerical optimization,modeling and system evaluation of a thermophotovoltaic hybrid panel

Photovoltaic (PV) solar panels generally produce electricity in the 6% to 16% efficiency range, the rest being dissipated in thermal losses. To recover this amount, hybrid photovoltaic thermal systems (PVT) have been devised. These are devices that simultaneously convert solar energy into electricity and heat. It is thus interesting to study the PVT system globally from different point of views in order to evaluate advantages and disadvantages of this technology and its possible uses. In particular in Chapter II, the development of the PVT absorber numerical optimization by a genetic algorithm has been carried out analyzing different internal channel profiles in order to find a right compromise between performance and technical and economical feasibility. Therefore in Chapter III ,thanks to a mobile structure built into the university lab, it has been compared experimentally electrical and thermal output power from PVT panels with separated photovoltaic and solar thermal productions. Collecting a lot of experimental data based on different seasonal conditions (ambient temperature,irradiation, wind...),the aim of this mobile structure has been to evaluate average both thermal and electrical increasing and decreasing efficiency values obtained respect to separate productions through the year. In Chapter IV , new PVT and solar thermal equation based models in steady state conditions have been developed by software Dymola that uses Modelica language. This permits ,in a simplified way respect to previous system modelling softwares, to model and evaluate different concepts about PVT panel regarding its structure before prototyping and measuring it. Chapter V concerns instead the definition of PVT boundary conditions into a HVAC system . This was made trough year simulations by software Polysun in order to finally assess the best solar assisted integrated structure thanks to F_save(solar saving energy)factor. Finally, Chapter VI presents the conclusion and the perspectives of this PhD work.

Packing a trunk : a physically based approach with full motional freedom

Geometric packing problems may be formulated mathematically as constrained optimization problems. But finding a good solution is a challenging task. The more complicated the geometry of the container or the objects to be packed, the more complex the non-penetration constraints become. In this work we propose the use of a physics engine that simulates a system of colliding rigid bodies. It is a tool to resolve interpenetration conflicts and to optimize configurations locally. We develop an efficient and easy-to-implement physics engine that is specialized for collision detection and contact handling. In succession of the development of this engine a number of novel algorithms for distance calculation and intersection volume were designed and imple- mented, which are presented in this work. They are highly specialized to pro- vide fast responses for cuboids and triangles as input geometry whereas the concepts they are based on can easily be extended to other convex shapes. Especially noteworthy in this context is our ε-distance algorithm - a novel application that is not only very robust and fast but also compact in its im- plementation. Several state-of-the-art third party implementations are being presented and we show that our implementations beat them in runtime and robustness. The packing algorithm that lies on top of the physics engine is a Monte Carlo based approach implemented for packing cuboids into a container described by a triangle soup. We give an implementation for the SAE J1100 variant of the trunk packing problem. We compare this implementation to several established approaches and we show that it gives better results in faster time than these existing implementations.

Optimization of RNA-based transgene expression by targeting Protein Kinase R

The aim of this thesis was to establish a method for repeated transfection of in vitro transcribed RNA (IVT-RNA) leading to a sustained protein expression lasting for days or even weeks. Once transfected cells recognize IVT-RNA as "non-self" and initiate defense pathways leading to an upregulated interferon (IFN) response and stalled translation. In this work Protein Kinase R (PKR) was identified as the main effector molecule mediating this cellular response. We assessed four strategies to inhibit PKR and the IFN response: A small molecule PKR inhibitor enhanced protein expression and hampered the induction of IFN-transcripts, but had to be excluded due to cytotoxicity. A siRNA mediated PKR knockdown and the overexpression of a kinase inactive PKR mutant elevated the protein expression, but the down-regulation of the IFN response was insufficient. The co-transfer of the viral inhibitors of PKR and the IFN response was most successful. The use of E3, K3 and B18R co-transfection enabled repeated IVT-RNA-based transfection of human fibroblasts. Thus, the developed protocol allows a continuous IVT-RNA encoded protein expression of proteins, which could be the basis for the generation of induced pluripotent stem cells (iPS) for several therapeutic applications in regenerative medicine or drug research.

Modelling, simulation and optimization of maintenance considerations on condition based maintenance

Globalization has increased the pressure on organizations and companies to operate in the most efficient and economic way. This tendency promotes that companies concentrate more and more on their core businesses, outsource less profitable departments and services to reduce costs. By contrast to earlier times, companies are highly specialized and have a low real net output ratio. For being able to provide the consumers with the right products, those companies have to collaborate with other suppliers and form large supply chains. An effect of large supply chains is the deficiency of high stocks and stockholding costs. This fact has lead to the rapid spread of Just-in-Time logistic concepts aimed minimizing stock by simultaneous high availability of products. Those concurring goals, minimizing stock by simultaneous high product availability, claim for high availability of the production systems in the way that an incoming order can immediately processed. Besides of design aspects and the quality of the production system, maintenance has a strong impact on production system availability. In the last decades, there has been many attempts to create maintenance models for availability optimization. Most of them concentrated on the availability aspect only without incorporating further aspects as logistics and profitability of the overall system. However, production system operator’s main intention is to optimize the profitability of the production system and not the availability of the production system. Thus, classic models, limited to represent and optimize maintenance strategies under the light of availability, fail. A novel approach, incorporating all financial impacting processes of and around a production system, is needed. The proposed model is subdivided into three parts, maintenance module, production module and connection module. This subdivision provides easy maintainability and simple extendability. Within those modules, all aspect of production process are modeled. Main part of the work lies in the extended maintenance and failure module that offers a representation of different maintenance strategies but also incorporates the effect of over-maintaining and failed maintenance (maintenance induced failures). Order release and seizing of the production system are modeled in the production part. Due to computational power limitation, it was not possible to run the simulation and the optimization with the fully developed production model. Thus, the production model was reduced to a black-box without higher degree of details.

Assessing the efficiency of a pharmacokinetic-based algorithm for target-controlled infusion of ketamine in ponies

The objective of this study was to assess a pharmacokinetic algorithm to predict ketamine plasma concentration and drive a target-controlled infusion (TCI) in ponies. Firstly, the algorithm was used to simulate the course of ketamine enantiomers plasma concentrations after the administration of an intravenous bolus in six ponies based on individual pharmacokinetic parameters obtained from a previous experiment. Using the same pharmacokinetic parameters, a TCI of S-ketamine was then performed over 120 min to maintain a concentration of 1 microg/mL in plasma. The actual plasma concentrations of S-ketamine were measured from arterial samples using capillary electrophoresis. The performance of the simulation for the administration of a single bolus was very good. During the TCI, the S-ketamine plasma concentrations were maintained within the limit of acceptance (wobble and divergence <20%) at a median of 79% (IQR, 71-90) of the peak concentration reached after the initial bolus. However, in three ponies the steady concentrations were significantly higher than targeted. It is hypothesized that an inaccurate estimation of the volume of the central compartment is partly responsible for that difference. The algorithm allowed good predictions for the single bolus administration and an appropriate maintenance of constant plasma concentrations.

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

Digital signal processing and digital system design using discrete cosine transform [student course]

The discrete cosine transform (DCT) is an important functional block for image processing applications. The implementation of a DCT has been viewed as a specialized research task. We apply a micro-architecture based methodology to the hardware implementation of an efficient DCT algorithm in a digital design course. Several circuit optimization and design space exploration techniques at the register-transfer and logic levels are introduced in class for generating the final design. The students not only learn how the algorithm can be implemented, but also receive insights about how other signal processing algorithms can be translated into a hardware implementation. Since signal processing has very broad applications, the study and implementation of an extensively used signal processing algorithm in a digital design course significantly enhances the learning experience in both digital signal processing and digital design areas for the students.

An Actor-Critic based controller for glucose regulation in type 1 diabetes

A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are (i) simultaneous adjustment of both the insulin basal rate and the bolus dose, (ii) initialization based on clinical procedures, and (iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of ±25% in the estimation of carbohydrates. The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children. The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycemic control. After optimization of the algorithm, the controller will be tested in a clinical trial.

Energy-Economical Heuristically Based Control of Compass Gait Walking on Stochastically Varying Terrain

Investigation uses simulation to explore the inherent tradeoffs ofcontrolling high-speed and highly robust walking robots while minimizing energy consumption. Using a novel controller which optimizes robustness, energy economy, and speed of a simulated robot on rough terrain, the user can adjust their priorities between these three outcome measures and systematically generate a performance curveassessing the tradeoffs associated with these metrics.

Object-Oriented Legacy System Trace-based Logic Testing

When reengineering legacy systems, it is crucial to assess if the legacy behavior has been preserved or how it changed due to the reengineering effort. Ideally if a legacy system is covered by tests, running the tests on the new version can identify potential differences or discrepancies. However, writing tests for an unknown and large system is difficult due to the lack of internal knowledge. It is especially difficult to bring the system to an appropriate state. Our solution is based on the acknowledgment that one of the few trustable piece of information available when approaching a legacy system is the running system itself. Our approach reifies the execution traces and uses logic programming to express tests on them. Thereby it eliminates the need to programatically bring the system in a particular state, and handles the test-writer a high-level abstraction mechanism to query the trace. The resulting system, called TESTLOG, was used on several real-world case studies to validate our claims.

TRIO LOGIC REGRESSION - DETECTION OF SNP - SNP INTERACTIONS IN CASE-PARENT TRIOS

Statistical approaches to evaluate higher order SNP-SNP and SNP-environment interactions are critical in genetic association studies, as susceptibility to complex disease is likely to be related to the interaction of multiple SNPs and environmental factors. Logic regression (Kooperberg et al., 2001; Ruczinski et al., 2003) is one such approach, where interactions between SNPs and environmental variables are assessed in a regression framework, and interactions become part of the model search space. In this manuscript we extend the logic regression methodology, originally developed for cohort and case-control studies, for studies of trios with affected probands. Trio logic regression accounts for the linkage disequilibrium (LD) structure in the genotype data, and accommodates missing genotypes via haplotype-based imputation. We also derive an efficient algorithm to simulate case-parent trios where genetic risk is determined via epistatic interactions.