Transient Exergy Destruction Analysis of a Vapor Compression System

Through online optimization and control, vapor compression systems (VCSs) can effectively respond to disturbances, such as weather or varying loads that cannot be accounted for at the design stage, while simultaneously maximizing system efficiency. However, to do so requires a mathematical characterization of efficiency for the VCS. In particular, we would like to maximize the exergetic efficiency of the VCS which characterizes system efficiency relative to the maximum achievable efficiency as postulated by the second law of thermodynamics. This is equivalent to minimizing the rate of exergy destruction during system operation. Furthermore, in applications where VCSs encounter high frequency disturbances, such as in refrigerated transport applications or passenger vehicles, optimizing efficiency at steady-state conditions alone may not lead to significant reductions in energy consumption. Therefore, it is necessary to model the transient effects of changes in control variables on the rate of exergy destruction in a given system. In this paper we derive an expression for the transient rate of exergy destruction for the refrigerant-side dynamics of a VCS. A lumped parameter moving boundary modeling framework is used to model the two heat exchangers in the VCS. Open loop simulations using a validated nonlinear model of an experimental VCS are presented to highlight how changes in individual control variables affect the component-level and system-level exergy destruction rates as a function of time. The results are discussed in the context of their implication for exergy destruction-based optimal control

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Today, intelligent machines \emph{interact and collaborate} with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.Comment: 20 page

Dynamic Modeling and Performance Analysis of Sensible Thermal Energy Storage Systems

In this paper we consider the problem of dynamic performance evaluation for sensible thermal energy storage (TES), with a specific focus on hot water storage tanks. We derive transient performance metrics from second law principles that can be used to guide real-time decision-making aimed toward improving demand response. We show how the transient nature of the metrics can be used not only to influence the values of control variables within the system, but also to mitigate adverse effects of disturbances during operation. To evaluate these metrics in the context of TES in hot water storage tanks, a thermal stratification model is needed. We derive a reduced order model which allows the simulation of tank thermal stratification during all modes of system operation. The proposed performance metrics are analyzed in simulation using the dynamic tank model. The results highlight key trade-offs captured by the metrics that can be incorporated into future optimal control design for sensible TES systems. Â

Dynamic Modeling and Validation of micro-CHP systems

Micro-Combined Heat and Power (micro-CHP) units locally generate electricity to simultaneously provide power and heat for residential buildings. Apart from the potential benefits of reducing carbon emissions and increasing robustness to brownouts and blackouts, micro-CHP systems can be controlled to meet energy demands. Micro-CHP systems consist of a prime mover that generates electricity, such as a fuel cell, an internal combustion engine, or a Stirling engine, and a waste heat recovery system that enables utilization of heat generated as a byproduct of electricity generation. Often, a thermal energy storage system is integrated with micro-CHP systems, thereby decoupling, in time, the recovery of the thermal energy from its utilization to meet demand. However, in order to effectively meet time-varying electricity and thermal demand through coordinated use of the prime mover and thermal energy storage system, the dynamics of each of these subsystems, and their interactions, need to be modeled. A low order dynamic model is derived for a micro-CHP system with a PEM (proton exchange membrane) fuel cell as the prime mover and a hot water tank as the thermal energy storage unit. Both steady-state and transient data is collected from an experimental micro-CHP testbed to validate the fuel cell and hot water tank models. Validation of the thermal energy storage model is performed for four distinct modes of operation: charging, discharging, simultaneous charging/discharging, and idling. Future work will include validation of the combined fuel cell and thermal energy storage models, as well as model-based control design for micro-CHP systems

Improving Human-Machine Collaboration Through Transparency-based Feedback – Part I: Human Trust and Workload Model

In this paper, we establish a partially observable Markov decision process(POMDP) model framework that captures dynamic changes in human trust and workload for contexts that involve interactions between humans and intelligent decision-aid systems. We use a reconnaissance mission study to elicit a dynamic change in human trust and workload with respect to the system’s reliability and user interface transparency as well as the presence or absence of danger. We use human subject data to estimate transition and observation probabilities of the POMDP model and analyze the trust-workload behavior of humans. Our results indicate that higher transparency is more likely to increase human trust when the existing trust is low but also is more likely to decrease trust when it is already high. Furthermore, we show that by using high transparency, the workload of the human is always likely to increase. In our companion paper, we use this estimated model to develop an optimal control policy that varies system transparency to affect human trust-workload behavior towards improving human-machine collaboration

Improving Human-Machine Collaboration Through Transparency-based Feedback – Part II: Control Design and Synthesis

To attain improved human-machine collaboration, it is necessary for autonomous systems to infer human trust and workload and respond accordingly. In turn, autonomous systems require models that capture both human trust and workload dynamics. In a companion paper, we developed a trust-workload partially observable Markov decision process (POMDP) model framework that captured changes in human trust and workload for contexts that involve interaction between a human and an intelligent decision-aid system. In this paper, we defne intuitive reward functions and show that these can be readily transformed for integration with the proposed POMDP model. We synthesize a near-optimal control policy using transparency as the feedback variable based on solutions for two cases: 1) increasing human trust and reducing workload, and 2) improving overall performance along with the aforementioned objectives for trust and workload. We implement these solutions in a reconnaissance mission study in which human subjects are aided by a virtual robotic assistant in completing a series of missions. We show that it is not always benefcial to aim to improve trust; instead, the control objective should be to optimize a context-specifc performance objective when designing intelligent decision-aid systems that infuence trust-workload behavior

Development of a Scale to Assess Communication Effectiveness of Managers Working in Multicultural Environments

Multicultural workplaces are the norm in the globalized business environment of the 21st century. With employees from diverse cultural backgrounds, global managers need to be sensitive to cultural differences and interact appropriately, avoiding miscommunication and misunderstanding and ensure business success. Communication effectiveness in interpersonal and intergroup encounters is influenced largely by a person’s ability to manage anxiety and uncertainty rising from a fear of the unknown. Though past research has addressed various aspects of intercultural communication, no single instrument addresses every aspect of intercultural complexities. This paper reports on the development and validation of a scale for measuring communication effectiveness of managers keeping in focus the important dimensions of anxiety and uncertainty management. The scale may be used both in the selection process of global managers as well as for determining their training interventions

Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions

Human trust in automation plays an essential role in interactions between humans and automation. While a lack of trust can lead to a human's disuse of automation, over-trust can result in a human trusting a faulty autonomous system which could have negative consequences for the human. Therefore, human trust should be calibrated to optimize human-machine interactions with respect to context-specific performance objectives. In this article, we present a probabilistic framework to model and calibrate a human's trust and workload dynamics during his/her interaction with an intelligent decision-aid system. This calibration is achieved by varying the automation's transparency---the amount and utility of information provided to the human. The parameterization of the model is conducted using behavioral data collected through human-subject experiments, and three feedback control policies are experimentally validated and compared against a non-adaptive decision-aid system. The results show that human-automation team performance can be optimized when the transparency is dynamically updated based on the proposed control policy. This framework is a first step toward widespread design and implementation of real-time adaptive automation for use in human-machine interactions.Comment: 21 page

Peanut oil press for developing countries

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (leaves 34-35).Despite the problems with obesity that the United States is facing today, malnutrition, caused in part by severely low dietary fat consumption, remains a problem among many people living in Sub-Saharan Africa. According to the World Health Organization, one third of people in developing countries are malnourished as well as vitamin or mineral deficient. While villagers do not have access to commercially produced vegetable oil (a common source of dietary fat), nor are industrial scale oil extraction methods appropriate for small scale production. As a result, they turn to traditional methods, such as a mortar and pestle, to extract oil from peanuts, sunflower seeds, and other oil bearing seeds and nuts. This process is both time and labor intensive, and still does not yield sufficient amounts of oil to satisfy the need for it. The need for a small scale press is clear. This thesis introduces a simple design which achieves a yield of 46.9 mL per cup (U.S.) which matches the yield produced using industrial technologies. This corresponds to 153% increase in yield and 38.5% increase in rate over using traditional methods such as a mortar and pestle. The design consists of two fixed plates connected by four rods, with a third plate which slides along the four guide rods.(cont.) A standard scissor jack is the mechanism by which the necessary pressure of 800-1000 psi is generated to extract the oil. A peanut container with a removable bottom holds the peanuts as they are pressed, and holes drilled into its cylindrical face allow the oil to spill out into a collection dish underneath the container. The entire design is compact, with a footprint of one square foot and a height of 22 inches. This is 12 times smaller than the Beilenberg ram press, the standard for small scale presses currently used in developing countries. Experimental results of the loading profile as function of time show that the jack does not need to be turned continuously once the oil begins to appear. This requires significantly less strength than current methods of oil extraction. Although future work is recommended to further develop and improve the press, it shows promise of alleviating the need for such a device in many impoverished parts of the world.by Neera Jain and Somin Lee.S.B