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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A viewgraph presentation describing the the six phases of a space mission is shown. The contents include: 1) What Does Planning Involve?; 2) Designing the Flight System; 3) Building the Flight System; 4) Testing the Flight System; 5) Flying the Mission; and 6) Analyzing the Data.
This presentation is expected to be given during the scheduled Communications Technology and Development discussion in the University Student Design Challenge (2). It is an introduction to various challenges inherent to the construction of networks in space. The presentation also includes both an overview of networking in general, as well as approaches taken to the construction of delay- and disruption- tolerant networks.
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Small spacecraft (smallsats) continue to find meaningful applications in the exploration of space and the Earth. Many agencies, companies, and universities are successfully exploiting these lower cost platforms, with their associated higher flight frequencies to rapidly develop and test novel technologies, conduct robotic exploration missions, and perform a wide range of science investigations. In addition to their lower costs to develop, integrate, launch, and operate, smallsats foster alternate approaches that can be used to conduct space research. This allows non-traditional individuals, groups, and organizations to contribute to space exploration and science applying novel management approaches spanning mission design, testing, and operations. Many of these approaches are based on collaborations and networks and have origins in related industries. Over time, a smallsat community has evolved to embody a new way of doing space. NASA, through various methods supports the development and growth of these communities, both within the agency and externally. This paper describes the processes, tools, and resources that NASA has developed and deployed to enable, inform, streamline, and sustain the smallsat community for the benefit of the space agency.
PNNL's Sensor Suitcase does an energy assessment of small buildings, enabling building owners to see which low-cost, energy-saving modifications would give them the best payoff. Implementing the recommended changes typically means 10% energy cost savings per small building. No other product provides such easy-to-use, expert-guided capabilities, and at half the cost of a typical energy assessment.
Fact sheet estimates the value of PV plus storage and discusses sizing and dispatching it for federal site managers.
Uncontrolled heat, air, and moisture transfer through the building enclosure has a significant impact on energy usage, comfort, indoor air quality, and building enclosure durability. Air leakage in commercial buildings in the U.S. accounts for about one quad (one quadrillion Btu) of energy annually, costing approximately $10 billion. As the thermal resistance of commercial building enclosures continues to improve, the relative contribution of air leakage to heating and cooling loads is increasing. A wide variety of air barrier technologies and construction practices to reduce the air leakage in buildings are available to the architect and designer. To promote more energy-efficient and durable building enclosure design, advances in easy-to-use tools for determining the impact of air leakage are needed. Oak Ridge National Laboratory (ORNL), the Air Barrier Association of America (ABAA), and the National Institute of Standards and Technology (NIST) partnered to develop an online calculator that estimates the potential energy, cost savings (due to energy use reduction), and moisture transport due to improvements in airtightness. The calculator estimates the energy and cost savings potential based on the pre-and post-retrofit air leakage rates for prototype commercial buildings. The tool does not include the energy and hygrothermal impacts of air intrusion or air that flows into and out of the building enclosure from the same side. This article reports on the development of the Energy Savings and Moisture Transfer Calculator. This online tool aims to fill this void, is based on the best science available, and is easy to use.
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Increased demand for cooling leads to consumption of a significant amount of energy by heating, ventilation, and air conditioning (HVAC) systems in buildings. Building envelope acts as a thermal barrier and plays a significant role in improving building energy efficiency. Radiant cooling systems often utilize building structure in the form of thermal storage and provide thermal comfort. The radiant cooling system has the potential of saving peak power in the buildings. In present study experimental and a simulation study is performed for chiller and cooling tower integrated chilled ceiling radiant cooling system for chilled water source and a parallel dedicated outdoor air system (DOAS) is used for providing latent load and fresh air requirements of the zone. Cooling tower based radiant cooling system is compared with a chiller-based radiant cooling system for achieving annual energy saving. Experiments are conducted for the chiller and cooling tower operated radiant cooling system. Based on experimental data, whole building simulation models of both cooling tower and chiller-based systems were calibrated. For both the simulation model of chiller and cooling tower operated radiant cooling system simulation were carried out for round the year. The annual simulation results show that the cooling tower operated radiant cooling system saves 14% energy as compared to the chilled water operated radiating cooling system.
Modern high-performance buildings exhibit an increasing number of building loads that use direct current (DC) electricity internally, rather than alternating current (AC), due to the advent of low-cost computing and advanced power electronics. Powering DC devices from the AC grid requires AC/DC power conversion, which introduces energy losses and reduces efficiency. As DC loads proliferate, the cumulative wasted energy associated with hundreds of millions of AC/DC converters has become one of the broadest energy savings opportunities in buildings. DC power distribution systems have been proposed as an elegant and transformative solution to the problem of DC devices. In a DC distribution system, the building's wires carry DC electricity, rather than AC; and a few centralized, highly efficient AC/DC converters replace the many smaller, less efficient converters that serve individual DC loads. Unfortunately, the trade-offs associated with DC distribution systems are not well understood. Reported energy savings associated with DC power distribution differ widely and have not been well validated. The Building Electrical Efficiency Analysis Model (BEEAM) is a Modelica library that simulates the efficiency of building electrical distribution systems using harmonic power flow. BEEAM can model a wide variety of building distribution topologies, including three-phase AC, single-phase AC, unipolar DC, bipolar DC, and hybrid networks under both balanced and unbalanced load conditions. BEEAM accurately models power electronic converter losses, provides granular estimates of losses throughout the distribution system, and properly models efficiency at part load conditions. Users can package BEEAM within a functional mockup unit (FMU), enabling co-simulation with other modeling platform, such as EnergyPlus. In summary, BEEAM provides a tool suite for fair and accurate comparison of the efficiency of building electrical distribution systems, including AC, DC, and hybrid systems.
The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.
Recently, there has been considerable research interest in the potential for DC distribution systems in buildings instead of the traditional AC distribution systems. Due to the need for performing power conversions between DC and AC electricity, DC distribution may provide electrical efficiency advantages in some systems. To support comparative evaluations of AC-only, DC-only, and hybrid AC/DC distribution systems in buildings, a new modeling toolkit called the Building Electrical Efficiency Analysis Model (BEEAM) was developed and is described in this paper. To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow, along with nonlinear device behavioral descriptions derived from empirical measurements. This paper describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior and device and system efficiencies using the toolkit are compared with experimental measurements of a small office environment in a variety of operating and load configurations. A detailed analysis of uncertainty estimation is also provided. Key findings were that a comparison of predicted versus measured efficiencies and power losses in the validation testbed using the initial toolkit implementation predicted device- and system-level efficiencies with reasonably good accuracy under both balanced and unbalanced AC scenarios. An uncertainty analysis also revealed that the maximum estimated error for system efficiency across all scenarios was 3%, and measured and modeled system efficiency agreed within the experimental uncertainty in approximately half of the scenarios. Based on the correspondence between simulation and measurement, the toolkit is proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC, and hybrid AC/DC distribution systems in buildings.
Alfalfa is a web based software service that implements a virtual building control system using the EnergyPlus simulation as a backend. Conventional building energy simulation is non interactive, meaning that a simulation is started and run to completion without any user input during the simulation. Alfalfa allows a client to start a building simulation, then interactively read sensor locations and dynamically command actuators in realtime. The Alfalfa web service conforms to the Project Haystack standard, which is a specification adopted by many building control system vendors. The service enables Haystack compliant control system vendors to test their control modules and algorithms without the need for a physical building or modifying their hardware and software, enabling faster and cheaper development cycles. Alfalfa has use cases beyond control system testing. One example is building fault detection, where physical building performance can be compared to simulated values in realtime, indicating where there may be a problem in the building. Another use case is informing predictive control systems, where near future events are simulated to premptively guide control decisions in the real building. This software is web based and designed to be self service. Users of Alfalfa can upload an OpenStudio building model and begin communicating with the simulated building without any advanced setup or configuration, as if they were communicating a real building control system. Behind the scenes, Alfalfa makes extensive use of Amazon Web Services, in order to scale from a very small number of users and buildings, to extremely large numbers in a cost effective way.
Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from Building Automation Systems are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. Here in this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC). STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The results also indicate that identifying and installing the few key sensor(s) is critical to cost-effectively improve FDD performance.
System modeling is critical when studying operation faults in chiller plants and boiler plants. However, current fault models have difficulties faithfully representing the operation of chiller plants and boiler plants under the effects of those faults, especially the control-related ones. In this study, we present a systematic method to develop high-fidelity models for approximating the behaviors of chiller plants and boiler plants under faulty conditions. Compared to existing ones, the resulting fault models have two advantages: first, they better characterize the dynamic patterns in the system operation. In those models, control architecture and control logic are faithfully implemented. Thus, they can be used to study control-related faults, such as incorrect staging control due to sensor bias and mistuned feedback control. Second, they are readily extensible and can support large-scale investigations to explore different faulty conditions/scenarios. Those models are established in a hierarchical structure while modules in each layer can be redeclared and parameterized at upper layers. In such a case, modifications to the models can be realized through model modifiers and the process can be easily streamlined with scripts. We applied the proposed models in a comprehensive fault impact evaluation of the 13 control-related faults of chiller and boiler plants. In this evaluation, the proposed plant model is coupled with the EnergyPlus thermal load model to study the impact of various faulty scenarios. Based on the evaluation results, we identified the faults that have the most significant impacts on the operation of the chiller and boiler plants, respectively. We also found that the relationship between the impacts of the studied faults and the severity level of the faults are highly non-linear
The research objective of this project is to design and demonstrate a low-cost, compact, easy-to-deploy, maintenance-free sensor node technology, and a network of such sensors, which enable the monitoring of multiphysical parameters and can transform today’s ordinary buildings into smart buildings with environmental awareness. We develop the sensor node and network via engineering and integration of existing technologies, including high-efficiency mechanical energy harvesting, and ultralow-power integrated circuits (ICs) for sensing and wireless communication. Through integration and innovative power management via specifically designed low-power control circuits for wireless sensing applications, and tailoring energy-harvesting components to indoor applications, the target products will have smaller volume, higher efficiency, and much lower cost (in both manufacturing and maintenance) than the baseline technology. Our development and commercialization objective is to create prototypes for our target products under the CWRU-Intwine collaboration.