Precision Cascade: A novel algorithm for multi-precision extreme compression
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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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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Time-sliced quantum circuit partitioning for modular architectures
Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and sparser connections between clusters. We exploit this clustering and the statically-known control flow of quantum programs to create tractable partitioning heuristics which map quantum circuits to modular physical machines one time slice at a time. Specifically, we create optimized mappings for each time slice, accounting for the cost to move data from the previous time slice and using a tunable lookahead scheme to reduce the cost to move to future time slices. We compare our approach to a traditional statically-mapped, owner-computes model. Our results show strict improvement over the static mapping baseline. Here, we reduce the non-local communication overhead by 89.8% in the best case and by 60.9% on average. Our techniques, unlike many exact solver methods, are computationally tractable.
Baker, Jonathan M.↗
GridDS: Data Science Toolkit for Energy Grid Data
According to the U.S. Energy Information Administration (EIA), the demand for energy is expected to increase 50% by the year 20501. While energy standards, such as the Institute of Electrical and Electronics Engineers (IEEE) Standard 1547, (Basso 2015) and monitoring with wide area management systems (WAMS) (Liu 2017, Zhou 2016) have enabled large scale data collection and storage, the application of this data in mitigating costs associated with increased consumer demand is an ongoing focus for energy research. This ubiquitous data collection presents a promising opportunity for machine learning and data science to improve efficiency of distributed energy resources (DERs). The GridDS software toolkit is designed to leverage advanced metering infrastructure (AMI), outage management systems data (OMS), Supervisory control Data Acquisition (SCADA), and geographic information systems (GIS) to forecast future energy demands and detect incipient grid failures. GridDS is a python software library designed to be modular and generalizable to data recorded by DERs. In adapting to disparate datasets recorded by various WAMS, GridDS provides a range of unique functionality not presently implemented in current WAMS which have highly specific software infrastructure by design. GridDS functionality ranges from data specification and preparation, to training and validation for state of the art machine learning, to interactive data visualization. For data intake, GridDS combines: Pandera: a library for creating data specifications. TimeScaleDB: a postgresSQL database infrastructure for efficient storage of timeseries data. Dataset class: A custom dataset class / interface that ensures modularity between a range of synthetic and live recorded datasets. Is
Ladd, Alexander↗
Development of a Vibration Technique Based on Geometric Optimization for Fatigue Life Evaluation of Sandwich Composite Structures
A major obstacle to obtaining cost-effective experimental data on the fatigue life of sandwich panels is the prohibitive amount of time and cost required to carry out millions of cycles. On the other hand, vibration techniques applied to sandwich geometries fail to match the stress patterns that are obtained from standard flexural fatigue tests. To overcome such limitations, a vibration-based fatigue technique is proposed, which entails the use of sandwich specimens whose geometries are optimized to reproduce the stress distribution observed during three point bend loading while vibrating at the first resonant frequency. The proposed vibration technique was experimentally validated. The results, compared with the average number of cycles to failure at different stress ratios obtained via the Three-Point Bending test, showed high levels of accuracy. The proposed method is robust and time effective and indicates the possibility of attaining fatigue lifetime prediction of a wide class of composite elements, such as sandwich panels.
36 MATERIALS SCIENCE↗
Design guide for space shuttle low-cost payloads
A handbook is presented which delineates the principles of the new low-cost design methodology for designers of unmanned payloads to be carried by the space shuttle. The basic relationships between payload designs and program cost effects are discussed, and some concepts for designing low-cost payloads and implementing low-cost programs are given. The data are summarized from a payloads effects study of three unmanned earth satellites (OAO, a syneq orbiter, and a small research satellite), and the earth satellite design is emphasized. Brief summaries of space shuttle and space tug performance, environmental, and interface data pertinent to low-cost payload concepts are included.
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Cost analysis of carbon dioxide concentrators
A methodology is developed to predict the relevant contributions of the more intangible cost elements encountered in the development of flight-qualified hardware and is used to predict the costs of three carbon dioxide concentration systems. The cost and performance data from Gemini, Skylab, and other programs are utilized as a basis for establishing the cost estimating relationships. The concentration systems analyzed are the molecular sieves C02 concentrator, the hydrogen-depolarized concentrator, and the regenerable solid desiccant concentrator. Besides the cost estimates for each system, their comparative criteria including relative characteristics, operational differences, and development status are considered.
Yakut, M. M.↗
Cost analysis of water recovery systems
Cost and performance data from Gemini, Skylab, and other aerospace and biotechnology programs were analyzed to identify major cost elements required to establish cost estimating relationships for advanced life support subsystems for long range planning in support of earth orbital programs. Cost analysis are presented for five leading water reclamation systems; (1) RITE waste management-water system;(2) reverse osmosis system;(3) multifiltration system;(4) vapor compression system; and(5) closed air evaporation system with electrolytic pretreatment.
Yakut, M. M.↗
Cost analysis of water recovery systems
A methodology was developed to predict the relevant contributions of the more intangible cost elements encountered in the development of flight-qualified hardware based on an extrapolation of past hardware development experience. Major items of costs within water recovery systems were identified and related to physical and/or performance criteria. Cost and performance data from Gemini, Skylab, and other aerospace and biotechnology programs were analyzed to identify major cost elements required to establish cost estimating relationships for advanced water recovery systems. The results of the study are expected to assist NASA in long-range planning and allocation of resources in a cost effective manner in support of earth orbital programs. This report deals with the cost analysis of the five leading water reclamation systems, namely: (1) RITE waste management-water system, (2) reverse osmosis system, (3) multifiltration system, (4) vapor compression system, and (5) closed air evaporation system with electrolytic pretreatment.
Yakut, M. M.↗
Low-Cost Spectral Sensor Development Description
Solar spectral data for all parts of the US is limited due in part to the high cost of commercial spectrometers. Solar spectral information is necessary for accurate photovoltaic (PV) performance forecasting, especially for large utility-scale PV installations. A low-cost solar spectral sensor would address the obstacles and needs. In this report, a novel low-cost, discrete-band sensor device, comprised of five narrow-band sensors, is described. The hardware is comprised of commercial-off-the-shelf components to keep the cost low. Data processing algorithms were developed and are being refined for robustness. PV module short-circuit current ($I_{sc}$) prediction methods were developed based on interaction-terms regression methodology and spectrum reconstruction methodology for computing $I_{sc}$. The results suggest the computed spectrum using the reconstruction method agreed well with the measured spectrum from the wide-band spectrometer (RMS error of 38.2 W/m 2 -nm). Further analysis of computed $I_{sc}$ found a close correspondence of 0.05 A RMS error. The goal is for ubiquitous adoption of the low-cost spectral sensor in solar PV and other applications such as weather forecasting.
Armijo, Kenneth Miguel↗
Life Cycle Cost Analysis of Shuttle-Derived Launch Vehicles, Volume 1
The design, performance, and programmatic definition of shuttle derived launch vehicles (SDLV) established by two different contractors were assessed and the relative life cycle costs of space transportation systems using the shuttle alone were compared with costs for a mix of shuttles and SDLV's. The ground rules and assumptions used in the evaluation are summarized and the work breakdown structure is included. Approaches used in deriving SDLV costs, including calibration factors and historical data are described. Both SDLV cost estimates and SDLV/STS cost comparisons are summarized. Standard formats are used to report comprehensive SDLV life cycle estimates. Hardware cost estimates (below subsystem level) obtained using the RCA PRICE 84 cost model are included along with other supporting data.
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Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results)
A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions. An economic experiment was carried out which will monitor citrus growers' decisions, actions, costs and losses, and meteorological forecasts and actual weather events and will establish the economic benefits of improved temperature forecasts. A summary is given of the economic experiment, the results obtained to date, and the work which still remains to be done. Specifically, the experiment design is described in detail as are the developed data collection methodology and procedures, sampling plan, data reduction techniques, cost and loss models, establishment of frost severity measures, data obtained from citrus growers, National Weather Service, and Federal Crop Insurance Corp., resulting protection costs and crop losses for the control group sample, extrapolation of results of control group to the Florida citrus industry and the method for normalization of these results to a normal or average frost season so that results may be compared with anticipated similar results from test group measurements.
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Leveraging large language models to address data scarcity in machine learning for graphene synthesis
Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.
Chemical vapor deposition↗
High-performance cementitious composites containing nanostructured carbon additives made from charred coal fines
Carbon-based nanomaterials, such as carbon nanoplatelets, graphene oxide, and carbon quantum dots, have many possible end-use applications due to their ability to impart unique mechanical, electrical, thermal, and optical properties to cement composites. Despite this potential, these materials are rarely used in the construction industry due to high material costs and limited data on performance and durability. In this study, domestic coal is used to fabricate low-cost carbon nanomaterials that can be used economically in cement formulations. A range of chemical and physical processing approaches are employed to control the size, morphology, and chemical functionalization of the carbon nanomaterial, which improves its miscibility with cement formulations and its impact on mechanical properties and durability. At loadings of 0.01 to 0.07 wt.% of coal-derived carbon nanomaterial, the compressive and flexural strength of cement samples are enhanced by 24% and 23%, respectively, in comparison to neat cement. At loadings of 0.02 to 0.06 wt.%, the compressive and flexural strength of concrete composites increases by 28% and 21%, respectively, in comparison to neat samples. Additionally, the carbon nanomaterial additives studied in this work reduce cement porosity by 36%, permeability by 86%, and chloride penetration depth by 60%. These results illustrate that low-loadings of coal-derived carbon nanomaterial additives can improve the mechanical properties, durability, and corrosion resistance of cement composites.
36 MATERIALS SCIENCE↗
Benchmarking Non-Hardware Balance of System (Soft) Costs for U.S. Photovoltaic Systems Using a Data-Driven Analysis from PV Installer Survey Results
This report presents results from the first U.S. Department of Energy (DOE) sponsored, bottom-up data-collection and analysis of non-hardware balance-of-system costs--often referred to as 'business process' or 'soft' costs--for residential and commercial photovoltaic (PV) systems.
14 SOLAR ENERGY↗
Data Movement in Data-Intensive High Performance Computing.
The cost of executing a floating point operation has been decreasing for decades at a much higher rate than that of moving data. Bandwidth and latency, two key metrics that determine the cost of moving data, have degraded significantly relative to processor cycle time and execution rate. Despite the limitation of sub-micron processor technology and the end of Dennard scaling, this trend will continue in the short-term making data movement a performance-limiting factor and an energy/power efficiency concern. Even more so in the context of large-scale and data-intensive systems and workloads. This chapter gives an overview of the aspects of moving data across a system, from the storage system to the computing system down to the node and processor level, with case study and contributions from researchers at the San Diego Supercomputer Center, the Oak Ridge National Laboratory, the Pacific Northwest National Laboratory, and the University of Delaware.
Data Movement↗
Onboard Experiment Data Support Facility (OEDSF): Conceptual design study
The Onboard Experimental Data Support Facility (OEDSF) is an inflight data processor based on a totally new architecture specifically developed to cost-effectively process the data of Shuttle payloads sensors. Processing data onboard fills the following needs: (1) reduction of data bulk by conversion to information (2)quick-look for evaluation, interactive operation, etc. (3) real-time computation of engineering representation of sensed phenomena. For example: Value of backscatter coefficient (sigma) of a scatterometer as a function of latitude and longitude (4) exploitation of the real-time availability of ancillary data, thereby obviating the need for time-tagging, recording, and recorrelation and (5) providing data or information immediately usable by the experimenter or user. The OEDSF is made up of modular and cascadable matrix processors. Each matrix has been sized to process the data of a full typical shuttle payload. Cost analyses indicate that significant savings are realized by processing data with the OEDSF compared with conventional ground facilities.
Source record↗
Lighting System Control Data to Improve Design and Operation: Tunable Lighting System Data from NICU Patient Rooms
The advancement of LED and controls technology, computing capacity, and software provides new opportunities for researchers and designers to work together to further optimize spaces for occupant benefit. Here, lighting system control data from five neonatal intensive care unit patient rooms was collected over a 25-week monitoring period and analyzed to better understand occupant response to a tunable lighting system with automatic transitions throughout the day. Lighting systems are very rarely refined after installation based on actual use. Objective data detailing how the lighting system is used by the actual occupants highlights the opportunities for optimization after installation and provides insight for improving the next design. As use of the data becomes more commonplace, it can be leveraged for design recommendations. The collection of the data required no additional cost beyond the time for examining the data. The analysis revealed several clear opportunities for improvement, including adjustments to the default control setting at night, re-labeling of the control stations, and adjustments to the nighttime fade rate. The patient room occupants were active users of the different zones, dimming options, and manual overrides made available by the lighting system.
60 APPLIED LIFE SCIENCES↗
Cost Modelling for Energy Efficient Window Replacements
This paper detailed data collection and cost-modeling efforts to understand the hardware and non-hardware (soft) cost of residential, energy efficient window replacements. NREL created a bottom-up cost model that details costs associated with window materials, installation labor, company overhead, and administrative costs. NREL did not find a single cost area for improvement but observed several non-cost barriers to increased window replacements. The non-cost barriers associated with window replacements are consumer education, lack of communication of the value of window replacement to consumers, and difficulty for consumers to identify quality products and installation.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗