Autonomous Integrated Receive System (AIRS) requirements definition. Volume 2: Design and development

Functional requirements and specifications are defined for an autonomous integrated receive system (AIRS) to be used as an improvement in the current tracking and data relay satellite system (TDRSS), and as a receiving system in the future tracking and data acquisition system (TDAS). The AIRS provides improved acquisition, tracking, bit error rate (BER), RFI mitigation techniques, and data operations performance compared to the current TDRSS ground segment receive system. A computer model of the AIRS is used to provide simulation results predicting the performance of AIRS. Cost and technology assessments are included

Aeromechanical stability analysis of COPTER

A plan was formed for developing a comprehensive, second-generation system with analytical capabilities for predicting performance, loads and vibration, handling qualities, aeromechanical stability, and acoustics. This second-generation system named COPTER (COmprehensive Program for Theoretical Evaluation of Rotorcraft) is designed for operational efficiency, user friendliness, coding readability, maintainability, transportability, modularity, and expandability for future growth. The system is divided into an executive, a data deck validator, and a technology complex. At present a simple executive, the data deck validator, and the aeromechanical stability module of the technology complex were implemented. The system is described briefly, the implementation of the technology module is discussed, and correlation data presented. The correlation includes hingeless-rotor isolated stability, hingeless-rotor ground-resonance stability, and air-resonance stability of an advanced bearingless-rotor in forward flight

Data driven TRL Transition Predictions for Early Technology Development in Defence

This paper proposes the framework of TRL (Technology Readiness Level) transition predictions for early technology development in defense. Though predicting future TRLs is an important planning tool, it has been studied less actively than the other critical issues on TRL, and previous studies mostly have resorted to domain experts. The proposed framework is data-driven and utilises both explanatory and predictive modelling techniques. As a case study, the proposed framework is applied to real technology development data from DTiMS (Defense Technology InforMation Service) which is identified as a key resource. The result of explanatory modelling shows that the two predictor variables, TRL before R&D and project cost, are statistically significant for future TRLs. Also, popular predictive models are fitted and compared with various performance indices using 10-fold cross validation. The two selected predictive models are linear regression and support vector machine models with the lowest prediction errors

Green buildings and design for adaptation: strategies for renovation of the built environment

The recent EU Directives 2010/31 and 2012/27 provide standards of nearly zero energy buildings for new constructions, aiming at a better quality of the built environment through the adoption of high-performance solutions. In the near future, cities are expected to be the main engine of development while bearing the impact of population growth: new challenges such as increasing energy efficiency, reducing maintenance costs of buildings and infrastructures, facing the effects of climate change and adjusting on-going and future impacts, require smart and sustainable approaches. To improve the capability of adaptation to dynamics of transformation, buildings and districts have to increase their resilience, assumed as ‘the capacity to adapt to changing conditions and to maintain or regain functionality and vitality in the face of stress or disturbance’ (Wilson A., Building Resilience in Boston, Boston Society of Architects, 2013). This paper describes the research methodology, developed by the Department of Architecture, a research unit of Technology for Architecture, to perform the assessment of resilience of existing buildings, as well as the outcomes of its application within Bologna urban context. This methodology focuses on the design for adaptation of social housing buildings, aiming at predicting their expected main impacts (energy consumption, emissions, efficiency, urban quality and environmental sustainability) and at developing models for renovation

Pressurization of cryogens: A review of current technology and its applicability to low-gravity conditions

A review of technology, history, and current status for pressurized expulsion of cryogenic tankage is presented. Use of tank pressurization to expel cryogenic fluids will continue to be studied for future spacecraft applications over a range of operating conditions in the low-gravity environment. The review examines experimental test results and analytical model development for quiescent and agitated conditions in normal-gravity, followed by a discussion of pressurization and expulsion in low-gravity. Validated, 1-D, finite difference codes exist for the prediction of pressurant mass requirements within the range of quiescent normal-gravity test data. To date, the effects of liquid sloshing have been characterized by tests in normal-gravity, but analytical models capable of predicting pressurant gas requirements remain unavailable. Efforts to develop multidimensional modeling capabilities in both normal and low-gravity have recently occurred. Low-gravity cryogenic fluid transfer experiments are needed to obtain low-gravity pressurized expulsion data. This data is required to guide analytical model development and to verify code performance

Innovation capability, disruptive technology and SME's performance in Malaysia

The aim of this study is to examine the relationship between innovation capacity (IC), and firm performance (FP) mediated by disruptive technology (DT) among SMEs within Selangor, Malaysia. The theoretical model is based on the Resource-Based Theory and the Theory of Innovation. To answer the research questions, four hypotheses were formulated. They are; (i) There is a significant relationship between innovative capacity and SMEs performance, (ii) There is a significant relationship between innovative capacity and disruptive technology, (iii) There is a significant relationship between disruptive technology and SMEs performance, (iv) There is a significant relationship between innovative capacity and SMEs performance mediated by disruptive technology. Self-administrated questionnaires were distributed to 800 owner-managers of SMEs in Selangor. A total of 150 firms responded in this study. This study utilised the Partial Least Squares Structural Equation Modelling (PLS-SEM) to establish validity and reliabiliy of measurement model and test the relationships. The results show a positive and significant relationship between innovation capacity and firm performance mediated by disruptive technology. Owner-manager of SMEs should emphasize innovativeness on all four (4) dimensions of innovations to ensure better firm performance. The results of this study also provides a better insight for various stakeholders to further understand the effects of IC and DT on SMEs performance. The study provides empirical evidence for theoretical relationship hypothesized in the research framework and also adds to knowledge on the importance of innovativeness in all aspects of firm’s offering along with adoptation of disruptive technologies in predicting firm performance. The study is confined to firm operating in Selangor. It is recommended that future research should also include other state(s) in Malaysia

Tank System Integrated Model: A Cryogenic Tank Performance Prediction Program

Accurate predictions of the thermodynamic state of the cryogenic propellants, pressurization rate, and performance of pressure control techniques in cryogenic tanks are required for development of cryogenic fluid long-duration storage technology and planning for future space exploration missions. This Technical Memorandum (TM) presents the analytical tool, Tank System Integrated Model (TankSIM), which can be used for modeling pressure control and predicting the behavior of cryogenic propellant for long-term storage for future space missions. Utilizing TankSIM, the following processes can be modeled: tank self-pressurization, boiloff, ullage venting, mixing, and condensation on the tank wall. This TM also includes comparisons of TankSIM program predictions with the test data andexamples of multiphase mission calculations

Examining Factors Affecting Customer Intention And Adoption Of Internet Banking In Jordan

This study aims to propose and validate a conceptual model demonstrating the main factors predicting customers’ intention and adoption of Internet banking in Jordan. The conceptual framework was theorized by integrating factors from the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2): performance expectancy (PE), facilitating conditions (FC), and hedonic motivation (HM) along with perceived risk (PR) and trust (TR) were extracted from Internet banking literature. A self-administrative questionnaire has been employed to collect data from a convenience sample of three hundred forty-eight Jordanian banking customers (69.6% response rate). By facilitating AMOS 21, a structural equation modelling (SEM) has been conducted to validate the proposed model and examine the research hypotheses. Statistical results approved that all predictors of behavioural intention (BI) have been recognized as significant. TR was the most influential factor predicting BI. This study ended by deliberating the research limitations and future directions

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%