Hydrogen infrastructure: resource evaluation and capacity modeling

A hydrogen economy could offer energy stability, economical, and environmental benefits. Several issues are involved in the design and implementation of a hydrogen economy such as the selection of feedstocks, generation and storage technologies, transportation methods, appropriate equipment capacity, codes/standards and public awareness. The design of a hydrogen infrastructure may seem insurmountable; however, as the system is deconstructed a proper design can be achieved. In order to better understand how a hydrogen system for light duty vehicles might operate, both hydrogen resource and capacity analysis and modeling is conducted. Specifically, an evaluation of leading near term production and distribution technologies is presented. A hydrogen system based on wind-generated electricity is then presented as a viable component in a hydrogen transition strategy. In support of this strategy, the theoretical hydrogen generation capability of a wind-hydrogen system on a state level basis is determined. A newsvendor framework is utilized to determine the optimal capacity for hydrogen filling stations based upon consumer behavior. The utility of this approach is expanded by including the effects of a competitive business environment by providing an alternative to the consumer and by including a consumer placed utility for hydrogen. The consumer placed utility represents the value to the consumer that the higher energy efficiency and environmental benefits hydrogen is perceived to provide. The results from a parametric analysis of key variables are presented in regards to inventory levels. The presented work provides an understanding as how a complex hydrogen economy might operate in the future. Finally, future areas for model expansion are presented --Abstract, page iii

Pacific variability reconciles observed and modelled global mean temperature increase since 1950

Global mean temperature change simulated by climate models deviates from the observed temperature increase during decadal-scale periods in the past. In particular, warming during the ‘global warming hiatus’ in the early twenty-first century appears overestimated in CMIP5 and CMIP6 multi-model means. We examine the role of equatorial Pacific variability in these divergences since 1950 by comparing 18 studies that quantify the Pacific contribution to the ‘hiatus’ and earlier periods and by investigating the reasons for differing results. During the ‘global warming hiatus’ from 1992 to 2012, the estimated contributions differ by a factor of five, with multiple linear regression approaches generally indicating a smaller contribution of Pacific variability to global temperature than climate model experiments where the simulated tropical Pacific sea surface temperature (SST) or wind stress anomalies are nudged towards observations. These so-called pacemaker experiments suggest that the ‘hiatus’ is fully explained and possibly over-explained by Pacific variability. Most of the spread across the studies can be attributed to two factors: neglecting the forced signal in tropical Pacific SST, which is often the case in multiple regression studies but not in pacemaker experiments, underestimates the Pacific contribution to global temperature change by a factor of two during the ‘hiatus’; the sensitivity with which the global temperature responds to Pacific variability varies by a factor of two between models on a decadal time scale, questioning the robustness of single model pacemaker experiments. Once we have accounted for these factors, the CMIP5 mean warming adjusted for Pacific variability reproduces the observed annual global mean temperature closely, with a correlation coefficient of 0.985 from 1950 to 2018. The CMIP6 ensemble performs less favourably but improves if the models with the highest transient climate response are omitted from the ensemble mean

Predictive Interfaces for Long-Distance Tele-Operations

We address the development of predictive tele-operator interfaces for humanoid robots with respect to two basic challenges. Firstly, we address automating the transition from fully tele-operated systems towards degrees of autonomy. Secondly, we develop compensation for the time-delay that exists when sending telemetry data from a remote operation point to robots located at low earth orbit and beyond. Humanoid robots have a great advantage over other robotic platforms for use in space-based construction and maintenance because they can use the same tools as astronauts do. The major disadvantage is that they are difficult to control due to the large number of degrees of freedom, which makes it difficult to synthesize autonomous behaviors using conventional means. We are working with the NASA Johnson Space Center's Robonaut which is an anthropomorphic robot with fully articulated hands, arms, and neck. We have trained hidden Markov models that make use of the command data, sensory streams, and other relevant data sources to predict a tele-operator's intent. This allows us to achieve subgoal level commanding without the use of predefined command dictionaries, and to create sub-goal autonomy via sequence generation from generative models. Our method works as a means to incrementally transition from manual tele-operation to semi-autonomous, supervised operation. The multi-agent laboratory experiments conducted by Ambrose et. al. have shown that it is feasible to directly tele-operate multiple Robonauts with humans to perform complex tasks such as truss assembly. However, once a time-delay is introduced into the system, the rate of tele\ioperation slows down to mimic a bump and wait type of activity. We would like to maintain the same interface to the operator despite time-delays. To this end, we are developing an interface which will allow for us to predict the intentions of the operator while interacting with a 3D virtual representation of the expected state of the robot. The predictive interface anticipates the intention of the operator, and then uses this prediction to initiate appropriate sub-goal autonomy tasks

Innovative Remote Smart Home for Immersive Engagement

An openly accessible, remotely operated smart home will be demonstrated as a tool for students to learn about residential energy usage and environmental impacts. Specifically, the demonstration unit provides classrooms an engaging experience that teaches students about energy efficiency technologies and how their behavior will have an impact on energy usage and the environment. It is expected that as students become aware of and understand how various energy efficiency technologies work barriers to their adoption will be lowered. The use of a web accessible, remote laboratory dramatically reduces lab setup time and equipment cost/space requirements for educators. Special attention is given to the web based interface to ensure the system is easy to use and requires only a standard web browser in order to operate. The interface also includes a video link so the user can feel that they are working with real hardware in real time and not using a simulation or virtual facility. An associated website provides a self-scheduling tool to provide access to the system and a resource for related background information on smart grid and residential energy efficiency technologies. In addition, supporting instructional materials that coincide with NGSS standards are available via download

A Novel HPLC Method for the Concurrent Analysis and Quantitation of Seven Water-Soluble Vitamins in Biological Fluids (Plasma and Urine): A Validation Study and Application

An HPLC method was developed and validated for the concurrent detection and quantitation of seven water-soluble vitamins (C, B1, B2, B5, B6, B9, B12) in biological matrices (plasma and urine). Separation was achieved at 30°C on a reversed-phase C18-A column using combined isocratic and linear gradient elution with a mobile phase consisting of 0.01% TFA aqueous and 100% methanol. Total run time was 35 minutes. Detection was performed with diode array set at 280 nm. Each vitamin was quantitatively determined at its maximum wavelength. Spectral comparison was used for peak identification in real samples (24 plasma and urine samples from abstinent alcohol-dependent males). Interday and intraday precision were <4% and <7%, respectively, for all vitamins. Recovery percentages ranged from 93% to 100%

Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces

Robonaut, the humanoid robot developed at the Dexterous Robotics Laboratory at NASA Johnson Space Center serves as a testbed for human-robot collaboration research and development efforts. One of the primary efforts investigates how adjustable autonomy can provide for a safe and more effective completion of manipulation-based tasks. A predictive algorithm developed in previous work was deployed as part of a software interface that can be used for long-distance tele-operation. In this paper we provide the details of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmic approach. We show that all of the algorithms presented can be optimized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. Judicious feature selection also plays a significant role in the conclusions drawn

Modeling and Classifying Six-Dimensional Trajectories for Teleoperation Under a Time Delay

Within the context of teleoperating the JSC Robonaut humanoid robot under 2-10 second time delays, this paper explores the technical problem of modeling and classifying human motions represented as six-dimensional (position and orientation) trajectories. A dual path research agenda is reviewed which explored both deterministic approaches and stochastic approaches using Hidden Markov Models. Finally, recent results are shown from a new model which represents the fusion of these two research paths. Questions are also raised about the possibility of automatically generating autonomous actions by reusing the same predictive models of human behavior to be the source of autonomous control. This approach changes the role of teleoperation from being a stand-in for autonomy into the first data collection step for developing generative models capable of autonomous control of the robot

Multiparty Electoral Competition in the Netherlands and Germany: A Model Based on Multinomial Probit

A typical assumption of electoral models of party competition is that parties adopt policy positions so as to maximize expected vote share. Here we use Euro-barometer survey data and European elite-study data from 1979 for the Netherlands and Germany to construct a stochastic model of voter response, based on multinomial probit estimation. For each of these countries, we estimate a pure spatial electoral voting model and a joint spatial model. The latter model also includes individual voter and demographic characteristics. The pure spatial models for the two countries quite accurately described the electoral response as a stochastic function of party positions. We use these models to perform a thought experiment so as to estimate the expected vote maximizing party positions. We go on to propose a model of internal party decision-making based both on pre-election electoral estimation and postelection coalition bargaining. This model suggests why the various parties in the period in question did not adopt vote maximizing positions. We argue that maximizing expected vote will not, in general, be a rational party strategy in multiparty political systems which are based on proportional representation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116246/1/pc98.pd