Search

How steroid hormone receptors (SHRs) orchestrate transcriptional activity remains only partly understood. Upon activation, SHRs bind the genome and recruit their co-regulators, crucial to induce gene expression. However, it remains unknown which components of the SHR-recruited co-regulator complex are essential to drive transcription following hormonal stimuli. Through a FACS-based genome-wide CRISPR screen, we comprehensively dissected the Glucocorticoid Receptor (GR) co-regulatory complex involved in gene-target regulation. We describe a novel functional cross-talk between PAXIP1 and the cohesin subunit STAG2 that is critical for regulation of gene expression by GR. Without altering the GR cistrome, PAXIP1 and STAG2 depletion alter the GR transcriptome, by impairing the recruitment of 3D-genome organization proteins to the GR complex. Importantly, we demonstrate that PAXIP1 is required for stability of cohesin on the genome, its localization to GR-occupied sites, and maintenance of enhancer-promoter interactions. Moreover, in lung cancer, where GR acts as tumor suppressor, PAXIP1/STAG2 loss enhances GR-mediated tumor suppressor activity by modifying local chromatin interactions. All together, we introduce PAXIP1 and STAG2 as novel co-regulators of GR, required to maintain 3D-genome architecture and drive the GR transcriptional programme following hormonal stimuli.

Summary Cohesin exists in two variants carrying either STAG/SA1 or SA2. Here we have addressed their specific contributions to the unique spatial organization of the mouse embryonic stem cell genome, which ensures super-enhancer-dependent transcription of pluripotency factors and repression of lineage-specification genes within Polycomb domains. We find that cohesin-SA2 facilitates Polycomb domain compaction through Polycomb repressing complex 1 (PRC1) recruitment and promotes the establishment of long-range interaction networks between distant Polycomb-bound promoters that are important for gene repression. Cohesin-SA1, in contrast, disrupts these networks, while preserving topologically associating domain (TAD) borders. The diverse effects of both complexes on genome topology may reflect two modes of action of cohesin. One, likely involving loop extrusion, establishes overall genome arrangement in TADs together with CTCF and prevents excessive segregation of same-class compartment regions. The other is required for organization of local transcriptional hubs such as Polycomb domains and super-enhancers, which define cell identity., Graphical Abstract, Highlights • Cohesin variants have distinct effects on mESC chromatin architecture and transcription • Cohesin-SA1 preferentially contributes to TAD boundary strength • Cohesin-SA2 facilitates Polycomb domain compaction through PRC1 recruitment • Cohesin-SA1 impedes and cohesin-SA2 promotes aggregation of distal Polycomb domains, The unique chromatin architecture of mESCs ensures high transcription of pluripotency factors and repression of lineage-specification genes. Cuadrado et al. report that besides their differential contribution to genome organization in TADs, cohesin variants SA1 and SA2 have antagonistic effects on the establishment of long-range contacts between Polycomb-bound genes that are essential for repression.

Two variant cohesin complexes containing SMC1, SMC3, RAD21 and either SA1 (also known as STAG1) or SA2 (also known as STAG2) are present in all cell types. We report here their genomic distribution and specific contributions to genome organization in human cells. Although both variants are found at CCCTC-binding factor (CTCF) sites, a distinct population of the SA2-containing cohesin complexes (hereafter referred to as cohesin-SA2) localize to enhancers lacking CTCF, are linked to tissue-specific transcription and cannot be replaced by the SA1-containing cohesin complex (cohesin-SA1) when SA2 is absent, a condition that has been observed in several tumors. Downregulation of each of these variants has different consequences for gene expression and genome architecture. Our results suggest that cohesin-SA1 preferentially contributes to the stabilization of topologically associating domain boundaries together with CTCF, whereas cohesin-SA2 promotes cell-type-specific contacts between enhancers and promoters independently of CTCF. Loss of cohesin-SA2 rewires local chromatin contacts and alters gene expression. These findings provide insights into how cohesin mediates chromosome folding and establish a novel framework to address the consequences of mutations in cohesin genes in cancer.

In addition to mediating sister chromatid cohesion, cohesin plays a central role in DNA looping and segmentation of the genome into contact domains (TADs). Two variant cohesin complexes that contain either STAG/SA1 or SA2 are present in all cell types. Here we addressed their specific contribution to genome architecture in non-transformed human cells. We found that cohesin-SA1 drives stacking of cohesin rings at CTCF-bound sites and thereby contributes to the stabilization and preservation of TAD boundaries. In contrast, a more dynamic cohesin-SA2 promotes cell type-specific contacts between enhancers and promoters within TADs independently of CTCF. SA2 loss, a condition frequently observed in cancer cells, results in increased intra-TAD interactions, likely altering the expression of key cell identity genes.

For storage of grid-scale electrical energy, redox-flow batteries (RFBs) are considered promising technologies. This paper explores the influence of electrolyte composition and ion transport on cell performance by using an integrated approach of experiments and cost modeling. In particular, the impact of the area-specific resistance on system capability is elucidated for the hydrogen/bromine RFB. The experimental data demonstrate very good performance with 1.46 W cm(-2) peak power and 4 A cm(-2) limiting current density at ambient conditions for an optimal cell design and reactant concentrations. The data and cost model results show that higher concentrations of RFB reactants do not necessarily result in lower capital cost as there is a tradeoff between cell performance and storage (tank) requirements. In addition, the discharge time and overall efficiency demonstrate nonlinear effects on system cost, with a 3 to 4 hour minimum discharge time showing a key transition to a plateau in terms of cost for typical RFB systems. The presented results are applicable to many different RFB chemistries and technologies and highlight the importance of ohmic effects and associated area-specific resistance on RFB viability.

Batteries are the key technology for enabling further mobile electrification and energy storage. Accurate prediction of the state of the battery is needed not only for safety reasons, but also for better utilization of the battery. In this work we present a state estimation strategy for a detailed electrochemical model of a lithium-ion battery. The benefit of using a detailed model is the additional information obtained about the battery, such as accurate estimates of the internal temperature, the state of charge within the individual electrodes, overpotential, concentration and current distribution across the electrodes, which can be utilized for safety and optimal operation. Based on physical insight, we propose an output error injection observer based on a reduced set of partial differential-algebraic equations. This reduced model has a less complex structure, while it still captures the main dynamics. The observer is extensively studied in simulations and validated in experiments for actual electric-vehicle drive cycles. Experimental results show the observer to be robust with respect to unmodeled dynamics as well as to noisy and biased voltage and current measurements. The available state estimates can be used for monitoring purposes or incorporated into a model based controller to improve the performance of the battery while guaranteeing safe operation.

Mathematical models of batteries which make use of the intercalation of a species into a solid phase need to solve the corresponding mass transfer problem. Because solving this equation can significantly add to the computational cost of a model, various methods have been devised to reduce the computational time. In this paper we focus on a comparison of the formulation, accuracy, and order of the accuracy for two numerical methods of solving the spherical diffusion problem with a constant or non-constant diffusion coefficient: the finite volume method and the control volume method. Both methods provide perfect mass conservation and second order accuracy in mesh spacing, but the control volume method provides the surface concentration directly, has a higher accuracy for a given numbers of mesh points and can also be easily extended to variable mesh spacing. Variable mesh spacing can significantly reduce the number of points that are required to achieve a given degree of accuracy in the surface concentration (which is typically coupled to the other battery equations) by locating more points where the concentration gradients are highest. (C) 2013 The Electrochemical Society. All rights reserved.

Lithium/air batteries, based on their high theoretical specific energy, are an extremely attractive technology for electrical energy storage that could make long-range electric vehicles widely affordable. However, the impact of this technology has so far fallen short of its potential due to several daunting challenges. In nonaqueous Li/air cells, reversible chemistry with a high current efficiency over several cycles has not yet been established, and the deposition of an electrically resistive discharge product appears to limit the capacity. Aqueous cells require water-stable lithium-protection membranes that tend to be thick, heavy, and highly resistive. Both types of cell suffer from poor oxygen redox kinetics at the positive electrode and deleterious volume and morphology changes at the negative electrode. Closed Li/air systems that include oxygen storage are much larger and heavier than open systems, but so far oxygen- and OH − -selective membranes are not effective in preventing contamination of cells. In this review we discuss the most critical challenges to developing robust, high-energy Li/air batteries and suggest future research directions to understand and overcome these challenges. We predict that Li/air batteries will primarily remain a research topic for the next several years. However, if the fundamental challenges can be met, the Li/air battery has the potential to significantly surpass the energy storage capability of today’s Li-ion batteries.

Lithium-ion (Li-ion) batteries are ubiquitous sources of energy for portable electronic devices. Compared to alternative battery technologies, Li-ion batteries provide one of the best energy-to-weight ratios, exhibit no memory effect, and have low self-discharge when not in use. These beneficial properties, as well as decreasing costs, have established Li-ion batteries as a leading candidate for the next generation of automotive and aerospace applications. In the automotive sector, increasing demand for hybrid electric vehicles (HEVs), plug-in HEVs (PHEVs), and EVs has pushed manufacturers to the limits of contemporary automotive battery technology. This limitation is gradually forcing consideration of alternative battery technologies, such as Li-ion batteries, as a replacement for existing leadacid and nickel-metal-hydride batteries. Unfortunately, this replacement is a challenging task since automotive applications demand large amounts of energy and power and must operate safely, reliably, and durably at these scales. The article presents a detailed description and model of a Li-ion battery. It begins the section "Intercalation-Based Batteries" by providing an intuitive explanation of the fundamentals behind storing energy in a Li-ion battery. In the sections "Modeling Approach" and "Li-Ion Battery Model," it present equations that describe a Li-ion cell's dynamic behavior. This modeling is based on using electrochemical principles to develop a physics-based model in contrast to equivalent circuit models. A goal of this article is to present the electrochemical model from a controls perspective.

© 2015 Elsevier B.V. The cost of a flow battery system can be reduced by increasing its power density and thereby reducing its stack area. If per-pass utilizations are held constant, higher battery power densities can only be achieved using higher flow rates. Here, a 3D computational fluid dynamics model of a flow battery flow field and electrode is used to analyze the implications of increasing flow rates to high power density operating conditions. Interdigitated and serpentine designs, and cell sizes ranging from 10 cm2 to 400 cm2, are simulated. The results quantify the dependence of pressure loss on cell size and design, demonstrating that the details of the passages that distribute flow between individual channels and the inlet and outlet have a major impact on pressure losses in larger cells. Additionally, in-cell flow behavior is analyzed as a function of cell size and design. Flow structures are interrogated to show how and where electrode parameters influence pressure drops, and how regions where transport is slow are correlated with the presence of experimentally observed cell degradation.

Fast parameter estimation is required in several applications where unforeseen changes often occur in system characteristics. Quite often such parameter estimation must be accompanied by state estimation as well. A prime example of growing importance is battery management systems for Lithium-ion batteries.Without accurate knowledge of internal battery states and parameters we are left with a choice between safety and performance with regards to battery selection for a specific application. With the goal of simultaneous estimation of both states and parameters, and arbitrarily fast estimation of the latter, a new adaptive observer based on matrix regressors is introduced in this paper. In contrast to earlier work, this adaptive observer requires the regressors include filters of much lower order. Sufficient conditions for the exponential convergence of parameter estimates to their true values are stated and derived. Guidelines are provided for the selection of design parameters, minimizing the number of observer parameters that must be tuned to facilitate fast convergence. Two numerical simulations are included, demonstrating the performance of the proposed observer scheme as well as a comparison with existing matrix regressor based adaptive observers. Examples considered are a generic linear plant model as well as the dynamics representative of a single particle of a Lithium-ion battery cell.

Spark assisted compression ignition (SACI) is currently under exploration as a combustion strategy to extend the operating range of homogeneous charge compression ignition (HCCI), which provides efficiency benefits over standard spark ignition (SI) combustion. This paper presents a physics-based control-oriented approach to modeling combustion in SACI. A double-Wiebe function is developed to capture the two-stage energy release seen in SACI, where a portion of the fuel is burned through flame propagation initiated by a spark, which then initiates auto-ignition in the remaining fuel. This double-Wiebe function is incorporated into a previously developed continuous-time model of HCCI combustion, and correlations for the Wiebe function parameters are developed based on physical model states. A simpler cycle-by-cycle HCCI model is also extended with a two-step energy release description for SACI combustion. Both models accurately capture the behavior of SACI and its sensitivity to different actuators when compared to experimental data.

In chemical engineering applications involving solvent removal from a concentrated polymer solution where diffusion is the governing process (e.g. dry spinning of synthetic silk-like fibres), the determination of the concentration-dependent diffusion coefficient is essential. Diffusion of solvent through such a polymer solution can be described by Fick's law. We propose a numerical procedure to obtain the dependence of the diffusion coefficient on solvent concentration. The procedure employs the finite element method to model a pan weighing experiment, in which a small amount of the polymer solution is placed in a pan and allowed to evaporate into air. In the computational model, the changing depth of the polymer solution is taken into account and the varying diffusion coefficient is determined so as to match the experimentally recorded mass loss. Copyright © 2006 John Wiley & Sons, Ltd.

Homogeneous charge compression ignition (HCCI) is a promising low-temperature combustion strategy for reducing NOx emissions and increasing efficiency in internal combustion engines. However, HCCI has no direct combustion initiator and, when achieved by reinducting or trapping residual exhaust gas with a variable valve actuation (VVA) system, becomes a dynamic process as the temperature of the residual gas couples one cycle to the next. These characteristics of residual-affected HCCI present a challenge for control engineers and a barrier to implementing HCCI in a production engine. In order to address these challenges, this paper outlines physics-based control strategies for both the VVA system and the HCCI combustion process. The results show that VVA system control can provide arbitrary valve timings on a cycle-to-cycle basis, enabling tight control of HCCI. By abstracting these valve timings further into an inducted gas composition and an effective compression ratio, model-based controllers can be developed to control simultaneously load and combustion timing in an HCCI engine.

By modulating engine valves to reinduct hot exhaust gas together with air and fuel, a clean and efficient form of autoignition can be created. Control of this combustion process, known as homogeneous charge compression ignition (HCCI), requires not only precise valve control but also a combustion control strategy that accounts for the cycle-to-cycle coupling through the exhaust. This paper outlines approaches for proving closed-loop stability of a valve controller and combustion controller using nonlinear analysis tools. Stability of the valve controller is shown using contraction analysis. Stability of the combustion controller is shown using sum of squares decomposition, convex optimization and the Positivstellensatz.

In novel combustion strategies like HCCI, fully flexible inlet and exhaust valves in terms of lift and timing are required to enable HCCI. In this paper, we explore issues related to control of an electrohydraulic variable valve system. A nonlinear model of an electrohydraulic valve system is linearized and simplified to a reduced order model to overcome numerical stability issues. A model based control algorithm is developed based on the reduced order model. The control algorithm consists of an observer to estimate the unmeasurable states and the disturbance acting on the plant, and permits tracking of any sufficiently smooth bounded trajectory. Numerical simulations and experimental results are presented for a sine wave and for a partial lift profile to indicate the effectiveness of the control algorithm developed.

A large class of problems in parameter estimation concerns nonlinearly parametrized systems. Over the past few years, a stability framework for estimation and control of such systems has been established. We address the issue of parameter convergence in such systems in this paper. Systems with both convex/concave and general parameterizations are considered. In the former case, sufficient conditions are derived under which parameter estimates converge to their true values using a min-max algorithm. In the latter case, to achieve parameter convergence a hierarchical min-max algorithm is proposed where the lower level consists of a min-max algorithm and the higher level component updates the bounds on the parameter region within which the unknown parameter is known to lie. Using this hierarchical algorithm, a necessary and sufficient condition is established for global parameter convergence in systems with a general nonlinear parameterization. In both cases, the conditions needed are shown to be stronger than linear persistent excitation conditions that guarantee parameter convergence in linearly parametrized systems. Explanations and examples of these conditions and simulation results are included to illustrate the nature of these conditions. A general definition of nonlinear persistent excitation that leads to parameter convergence is proposed at the end of this paper.

In this work, we apply the projection-based model-reduction framework for PDEs of [1] to the diffusion process governing the intercalation of lithium ions into spherical particles that appears in electrochemical models of Li-ion batteries. We first invoke the projection framework with different projections (the so-called natural-i.e. Galerkin-and volume-averaging projections-NP and VAP, respectively) and different basis sets (even monomials and eigenfunctions of the PDE) to arrive at different reduced models, which we then benchmark in a dimensionless frequency-domain setting. Surprisingly, we find that VAP of an even monomial basis yields an unstable reduced model for anything but the lowest order; however, NP yields suitable results for the basis sets and orders considered. Furthermore, we find that the NP-eigenfunction combination makes for a slow convergence when compared to NP with even monomials; however, room for further improvement is seen when compared with a balanced truncation, which is not applicable in the nonlinear setting of concentration-dependent diffusivity.

This paper concerns adaptive estimation of dynamic systems which are nonlinearly parameterized. A majority of adaptive algorithms employ a gradient approach to determine the direction of adjustment, which ensures stable estimation when parameters occur linearly. These algorithms, however, do not suffice for estimation in systems with nonlinear parameterization. We introduce in this paper a new algorithm for such systems and show that it leads to globally stable estimation by employing a different regression vector and selecting a suitable step size. Both concave/convex parameterizations as well as general nonlinear parameterizations are considered. Stable estimation in the presence of both nonlinear parameters and linear parameters which may appear multiplicatively is established. For the case of concave/convex parameterizations, parameter convergence is shown to result under certain conditions of persistent excitation.

This paper deals with adaptive control of a class of second-order nonlinear systems with a triangular structure and convex/concave parameterization. In Annaswamy et al. (Automatica 33(11) (1998) 1975 –1995) it was shown that nonlinearly parameterized systems that satisfy certain matching conditions can be adaptively controlled in a stable manner. In this paper, we relax these matching conditions and include additional dynamics between the nonlinearities and the control input. Global boundedness and convergence to within a desired precision e is established. No overparameterization of the adaptive controller is required.

Homogeneous charge compression ignition (HCCI) provides improved efficiency and emissions relative to current engine technologies. One of the barriers to implementing HCCI on production engines is the development of a robust control strategy to transition from traditional spark-ignition (SI) mode to HCCI mode and back. This paper presents such a strategy, based on the control of combustion phasing using fuel injection timing during the mode switch from SI to HCCI. The controller is based on a cycle-by-cycle combustion model developed in previous work. In order to obtain a state estimator for both modes, the model is linearized around operating points corresponding to the steady-states before (SI) and after (HCCI) the switch. The linearized HCCI model is used to synthesize a closed-loop controller to track a desired combustion phasing, with fuel injection timing as the controlled input. The control strategy is tested on a single-cylinder HCCI engine with direct injection. Experimental results at different operating points show that the controller is able to maintain a desirable phasing transient during the mode switch, prevent cycles with very early or late phasing and enable smooth transitions with minimal load fluctuations.Copyright © 2013 by ASME

Li-ion based batteries are believed to be the most promising battery system for HEV/PHEV/EV applications due to their high energy density, lack of hysteresis and low self-discharge currents. However, designing a battery, along with its Battery Management System (BMS), that can guarantee safe and reliable operation, is a challenge since aging and other mechanisms involving optimal charge and discharge of the battery are not sufficiently well understood. In a previous article [1], we presented a model that has been studied in [2]–[5] to understand the operation of a Li-ion battery. In this article, we continue our work and present an approximation technique that can be applied to a generic battery model. These approximation method is based on projecting solutions to a Hilbert subspace formed by taking the span of an countably infinite set of basis functions. In this article, we apply this method to the key diffusion equation in the battery model, thus providing a fast approximation for the single particle model (SPM) for both variable and constant diffusion case.Copyright © 2013 by ASME

Elastic-plastic deformation of a thin-walled pipe, composed of layers with material orthotropic in the elastic and plastic domains, is analyzed. The first material direction for each layer is inclined for an angle (+a and -a successively) with respect to the pipe axis. The yield condition of the material represents a generalization of Hill's criterion to include material hardening. The pipe is loaded by internal pressure and other external loads, and it is supposed that the pipe cross section is free to expand or contract. The derived incremental relations for stress integration, which take into account the stress-strain conditions in the pipe wall, are based on the governing parameter method, developed by the first author, where the problem of implicit integration of inelastic constitutive relations within a tune (load) step is reduced to solution of one governing nonlinear equation. Also, the expressions for the tangent elastic-plastic constitutive matrix are derived. One solved simple numerical example demonstrates the main characteristics of the developed algorithm, especially suited for a general elastic-plastic analysis of composite pipes (within the displacement-based finite element method). B, N C e, ein p S Nomenclature = material constants = tangent constitutive matrix = total and inelastic strains = governing parameter = deviatoric stress = internal variable = increment of plastic strain = stress

There is a strong need for advanced control methods in battery management systems, especially in the plug-in hybrid and electric vehicles sector, due to cost and safety issues of new high-power battery packs and high-energy cell design. Limitations in computational speed and available memory require the use of very simple battery models and basic control algorithms, which in turn result in suboptimal utilization of the battery. This work investigates the possible use of optimal control strategies for charging. We focus on the minimum-time charging problem, where different constraints on internal battery states are considered. Based on features of the open-loop optimal charging solution, we propose a simple one-step predictive controller, which is shown to recover the time-optimal solution, while being feasible for real-time computations. We present simulation results suggesting a decrease in charging time by 50% compared to the conventional constant-current / constant-voltage method for lithium-ion batteries.

It is well-known that an HCCI-based engine has a high operating efficiency and low emissions. However, the HCCI process is sensitive to disturbances that can cause the engine to misfire, especially at low loads. In this paper, we propose an efficient, physics-based model for studying engine state evolution in the presence of misfires. The model relies on a simple recursive mass balance model, simplified thermodynamic mechanisms, and use of the “integrated Arrhenius rate” with a modified threshold to model combustion phasing, occurrence of misfire, and partial burn during misfire. Simulation results for our reduced-order model, compared to a high-resolution HCCI simulation proposed in a previous work, show good match.

ElectroHydraulic Valve Systems (EHVS) have the ability to vary valve profiles of internal combustion engines on a per cycle basis, thus allowing one to optimize the combustion process and improve its efficiency and emissions characteristics. Such a capability makes an EHVS system a very suitable actuation mechanism for novel combustion strategies. However, designing a controller which ensures that the EHVS executes the desired valve profiles is challenging due to the required fast response time, the inherent non-linearity of the system, and the time delays associated with the servo valves, actuators and the interconnections. In this paper, we explore a systematic and semi-automatic way to design a controller for an EHVS. The controller has a feedforward and feedback structure. The feedforward controller is obtained by using an on-line system identification scheme, while the feedback controller is based on an off-line system identification model. This procedure is applied successfully to a laboratory EHVS engineered by Team Corporation covering the full range of engine operating speeds from 500 to 4000rpm.

Emerging drivetrains open new possibilities in vehicle stability control. In this paper, we consider a vehicle powered by four independently driven wheel motors, one for each wheel, with steer-by-wire. We explore the possibilities of using steering and individual wheel acceleration on top of braking to enhance stability and steerability. A 7-DOF model is developed to capture the vehicle longitudinal, lateral and yaw motion and the other four degrees of motion representing the wheel dynamics. However, this 7-DOF model is rather complicated for controller design due to the coupling of the inputs, and because the tire forces are highly nonlinear and coupled when the tires are at their limits. A control oriented model is developed that decouples the inputs and facilitates ease in control algorithm development. In addition, the longitudinal and lateral forces are also decoupled based on detailed analysis of the tire behavior at their limits. Simulation results of both the 7-DOF model and the control oriented model are compared to show that the control oriented model is capable of capturing the vehicle behavior at safety-critical cases.