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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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In this paper, I describe software product lines and why a Ground Data Processing System should use one. I also describe how to develop a software product line, using examples from an imaginary Ground Data Processing System.
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Some alternative approaches to constructing a large remote-sensing data system are explored. The cost and performance implications of using a collection of 'small' computers versus using one large computer are examined. Several architectures and associated costs for the large data system supporting a single data center are presented. Recurring cost factors (maintenance and operations) currently slightly favor the single large-machine architecture, but other factors may dictate the choice of one of the two multimachine architectures discussed.
This publication includes 18 composite data products (CDPs) for fuel cell technology status, focusing on state-of-the-art fuel cell voltage durability and cost with data through the fourth quarter of 2017.
Cloud computing has the potential to bring high performance computing capabilities to the average science researcher. However, in order to take full advantage of cloud capabilities, the science data used in the analysis must often be reorganized. This typically involves sharding the data across multiple nodes to enable relatively fine-grained parallelism. This can be either via cloud-based file systems or cloud-enabled databases such as Cassandra, Rasdaman or SciDB. Since storing an extra copy of data leads to increased cost and data management complexity, NASA is interested in determining the benefits and costs of various cloud analytics methods for real Earth Observation cases. Accordingly, NASA's Earth Science Technology Office and Earth Science Data and Information Systems project have teamed with cloud analytics practitioners to run a benchmark comparison on cloud analytics methods using the same input data and analysis algorithms. We have particularly looked at analysis algorithms that work over long time series, because these are particularly intractable for many Earth Observation datasets which typically store data with one or just a few time steps per file. This post will present side-by-side cost and performance results for several common Earth observation analysis operations.
A viewgraph presentation on the design of a low cost unmanned aerial vehicle (UAV) doppler radar data system is presented. The topics include: 1) Science and Mission Background; 2) Radar Requirements and Specs; 3) Radar Realization: RF System; 4) Processing of RF Signal; 5) Data System Design Process; 6) Can We Remove the DSP? 7) Determining Approximate Speed Requirements; 8) Radar Realization: Data System; 9) Data System Operation; and 10) Results.
Cost and time to extract land use maps, natural-resource surveys, and other data from aerial and satellite photographs are reduced by diazo processing. Process can be controlled to enhance features such as vegetation, land boundaries, and bodies of water.
The work described in this paper details the development of a low-cost, short-development time data acquisition and processing system for a coherent Doppler lidar. This was done using common laboratory equipment and a small software investment. This system provides near real-time wind profile measurements. Coding flexibility created a very useful test bed for new techniques.
In-situ analysis on the output data of scientific simulations has been made necessary by ever-growing output data volumes and increasing costs of data movement as supercomputing is moving towards exascale. With hardware accelerators like GPUs becoming increasingly common in high end machines, new opportunities arise to co-locate scientific simulations and online analysis performed on the scientific data generated by the simulations. However, the asynchronous nature of GPGPU programming models and the limited context-switching capabilities on the GPU pose challenges to co-locating the scientific simulation and analysis on the same GPU. This paper dives deeper into these challenges to understand how best to co-locate analysis with scientific simulations on the GPUs in HPC clusters. Specifically, our 'Landrush' approach to GPU sharing proposes a solution that utilizes idle cycles on the GPU to provide an improved time-to-answer, that is, the total time to run the scientific simulation and analysis of the generated data. Landrush is demonstrated with experimental results obtained from leadership high-end applications on ORNL's Titan supercomputer, which show that (i) GPU-based scientific simulations have varying degrees of idle cycles to afford useful analysis task co-location, and (ii) the inability to context switch on the GPU at instruction granularity can be overcome by careful control of the analysis kernel launches and software-controlled early completion of analysis kernel executions. Results show that Landrush is superior in terms of time-to-answer compared to serially running simulations followed by analysis or by relying on the GPU driver and hardwired thread dispatcher to run analysis concurrently on a single GPU.
Multiple Solar Electric Propulsion Technology Demonstration Mission were developed to assess vehicle performance and estimated mission cost. Concepts ranged from a 10,000 kilogram spacecraft capable of delivering 4000 kilogram of payload to one of the Earth Moon Lagrange points in support of future human-crewed outposts to a 180 kilogram spacecraft capable of performing an asteroid rendezvous mission after launched to a geostationary transfer orbit as a secondary payload. Low-cost and maximum Delta-V capability variants of a spacecraft concept based on utilizing a secondary payload adapter as the primary bus structure were developed as were concepts designed to be co-manifested with another spacecraft on a single launch vehicle. Each of the Solar Electric Propulsion Technology Demonstration Mission concepts developed included an estimated spacecraft cost. These data suggest estimated spacecraft costs of $200 million - $300 million if 30 kilowatt-class solar arrays and the corresponding electric propulsion system currently under development are used as the basis for sizing the mission concept regardless of launch vehicle costs. The most affordable mission concept developed based on subscale variants of the advanced solar arrays and electric propulsion technology currently under development by the NASA Space Technology Mission Directorate has an estimated cost of $50M and could provide a Delta-V capability comparable to much larger spacecraft concepts.
A techno-economic analysis is underway examining the cost and performance of future large-scale photovoltaic (PV) plant components, including bifacial modules, tandem modules, increased plant voltage architectures, and module-level power electronics. Integration of these components into PV plant designs is compared with current PV technologies based on levelized cost of electricity (LCOE). Baseline models are developed and validated against recorded PV plant performance data. Expected cost and performance data of future PV technologies are incorporated into the baseline models. An evolutionary algorithm is utilized to optimize PV plant configuration, technology combination, and LCOE. Furthermore, this paper focuses on the bifacial module analysis.
Technology trends have resulted in widely different rates of improvement in computational throughput as compared to data movement rates in computer systems. With future systems, the cost of data movement through the memory hierarchy is expected to become even more dominant relative to the cost of performing arithmetic operations, both in terms of time and energy. Therefore, optimizing data access costs will become ever more critical in the coming years. However, unlike the well understood model of computational complexity in terms of the number of elementary operations to execute an algorithm, the inherent data-movement complexity of computations is unknown for most algorithms. Given the crucial importance of optimizing data access costs in scalable parallel systems, it is of great interest to develop techniques for understanding the inherent data movement complexity of an algorithm. In this project, we focus on this fundamental and significant problem.
Data services such as search, discovery, and management in scalable distributed environments have traditionally been decoupled from the underlying file systems, and are often deployed using external databases and indexing services. However, modern data production rates, looming data movement costs, and the lack of metadata, entail revisiting the decoupled file system-data services design philosophy.In this paper, we present TagIt, a scalable data management service framework aimed at scientific datasets, which is tightly integrated into a shared-nothing distributed file system. A key feature of TagIt is a scalable, distributed metadata indexing framework, using which we implement a flexible tagging capability to support data discovery. The tags can also be associated with an active operator, for pre-processing, filtering, or automatic metadata extraction, which we seamlessly offload to file servers in a load-aware fashion. Our evaluation shows that TagIt can expedite data search by up to 10X over the extant decoupled approach.
The running costs of data centers are dominated by the need to dissipate heat generated by thousands of server machines. Higher temperatures are undesirable as they lead to premature silicon wear-out; in fact, mean time to failure has been shown to decrease exponentially with temperature (Black's law). Although other server components also generate heat, microprocessors still dominate in most server configurations and are also the most vulnerable to wearout as the feature sizes shrink. Even as processor complexity and technology scaling have increased the average energy density inside a processor to maximally tolerable levels, modern microprocessors make extensive use of hardware structures such as the load-store queue and other CAM-based units, and the peak temperatures on chip can be much worse than even the average temperature of the chip. In recent studies, it has been shown that hot-spots inside a processor can generate {approx} 800W/cm{sup 2} heat flux whereas the average heat flux is only 10-50W/cm{sup 2}, and due to this disparity in heat generation, the temperature in hot spots may be up to 30 C more than average chip temperature. The key problem processor hot-spots create is that in order to prevent some critical hardware structures from wearing out faster, the air conditioners in a data center have to be provisioned for worst case requirements. Worse yet, air conditioner efficiencies decrease exponentially as the desired ambient temperature decreases relative to the air outside. As a result, the global cooling costs in data centers, which nearly equals the IT equipment power consumption, are directly correlated with the maximum hot spot temperatures of processors, and there is a distinct requirement for a cooling technique to mitigate hot-spots selectively so that the global air conditioners can operate at higher, more efficient, temperatures. We observe that localized cooling via superlattice microrefrigeration presents exactly this opportunity whereby hot-spots can be cooled selectively and allow global coolers to operate at much more efficient temperatures. Recent advances in processor cooling technologies have demonstrated that thermoelectric coolers (TEC), which use a Peltier effect to form heat pumps, can be used to reduce the temperature of hot spots. By applying a thermoelectric cooler between the heat spreader and the processor die and applying current selectively at the hot spots, heat from the hot-spots can be spread much more efficiently. The ability to implement such thermoelectric coolers on a real silicon device has been demonstrated recently, albeit for small prototype chips. The key question then, that needs to be answered before such thermoelectric coolers can be integrated in commodity server processors, is 'What is the potential for superlattice microrefrigeration to reduce global cooling costs in data centers?'. In order to answer this question, we present a comprehensive analysis of the impact of thermoelectric coolers on global cooling costs. Our thermal analysis covers all aspects of cooling a server in a data center, and integrates on-chip dynamic and leakage power sources with a detailed heat diffusion model of a processor (that models the silicon to the thermoelectric cooler to the heat spreader and the heat sink) and finally the computer room air conditioner (CRAC) efficiency, as shown in Figure 1. In Section II, we present the components of the system model.
This submission includes an Electricity Generation Summary, Maintenance Logs, Detailed Operations Data, Operating Cost Summary, and an Operations overview at the Paisley Oregon Geothermal Plant. Data uploaded for SVEC by Tom Williams, NREL
Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we consider training sets constructed from coupled-cluster (CC) and density functional theory (DFT) data. We report that multitask surrogates can predict at CC-level accuracy with a reduction in data generation cost by over an order of magnitude. Of note, our approach allows the training set to include DFT data generated by a heterogeneous mix of exchange–correlation functionals without imposing any artificial hierarchy on functional accuracy. More generally, the multitask framework can accommodate a wider range of training set structures—including the full disparity between the different levels of fidelity—than existing kernel approaches based on Δ-learning although we show that the accuracy of the two approaches can be similar. Consequently, multitask regression can be a tool for reducing data generation costs even further by opportunistically exploiting existing data sources.
A major portion of time during the tactical operation of Mars rovers is spent for selecting, prioritizing, and coordinating sciences and engineering activities such that they fit within resource constraints, including the downlink data volume, energy, and time. In particular, the downlink data volume constraint is getting particularly tighter in recent missions because modern instruments produce increasingly high data volume while the communication bandwidth is essentially bounded by the law of physics. Tactical operation would be substantially simplified, hence the operation cost could be reduced, if the data volume constraint is relaxed or even removed. In this abstract, we propose a new operation paradigm for achieving this goal. The key observation is that, both in science and engineering applications, the bit size of raw data is typically much greater than the volume of processed information that is needed for scientific or engineering analysis. For example, a full-resolution image from Mastcam-Z, the main science camera on Perseverance, is about 700 kB in volume and we downlinked 29,685 images up to Sol 243, totaling ~20 GB of data. But of course, scientists do not use every pixel of these images; what they really look for in the images are geological features, typically represented by specific geometric configurations or textures. An end product after processing hundreds of Mascam-Z images could be a single geological map summarizing the spatial distribution of the features. For another example, a 100-meter drive of Perseverance produces 7-12 MB of drive telemetry, which records every detail of the rover's motion at 8 Hz, including position, attitude, steering angles, encoder readings, motor currents and many other information. But what the ground engineers eventually pay attention to is the signs of anomaly, such as excessive motor currents or high slip; if a drive is nominal, the vast majority of this data is unused. What if, then, we process the raw data onboard and only downlink the processed data that is relevant to scientific or engineering analyses, such as a list of detected science features (with cropped images) or a list of potential signs of anomaly while driving? A major roadblock for such onboard, high-level information processing has been the onboard computational resource. RAD750, the main onboard computer of Perseverance, is obviously not sufficient for performing complex image or signal processing such as object detection, semantic segmentation, or anomaly detection. Interestingly, RAD750 is not the best processor that Perseverance has; Qualcomm's Snapdragon 801, a modern mobile processor, is on her Heli Base Station, a device for communicating with Mars Helicopter Ingenuity; also, Intel's Atom E3845 processors are on engineering cameras. In the reminder of this paper, we will introduce two particular uses cases of these high-performance co-processors (meaning auxiliary CPU, GPU, or other types of processors that are separate from the main processor that runs the main flight software) for lowering operation cost and accommodating more science activities for a given communication constraint.
An economic model was developed that incorporates spatially varying joint yield and price distributions for the multiple crop choices a farmer faces when choosing between conventional and bioenergy crops. The model is developed in Matlab, and has options for no, annual and upfront payment results.