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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Loaded horizontal antenna over an imperfect ground

The effect of loading for antennas over an imperfect ground is analyzed. The Fresnel reflection coefficient technique is used to derive an E-integral equation for the antenna current. An optimization method is employed to determine the critical loading parameter which enforces a traveling wave current on the antenna. Results are given for the critical loading parameters, antenna currents, input impedances, and radiation patterns for various antenna dimensions and ground parameters. Calculations of the time-domain behavior of the current are presented to show the effect of loading on antenna transient response.

Rahmat-Samii, Y.↗
DOE OSTI2018

Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings

Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Discovering and Demonstrating a Novel High-Performing 2D-Patterned Electrode for Proton-Exchange Membrane Water Electrolysis Devices

Proton-exchange membrane water electrolysis (PEMWE) produces hydrogen with high efficiency and purity but uses high-loading platinum-group metal (PGM) catalysts. Such concerns call for the development of novel electrode architectures to improve catalyst utilization and mass activity, thus promoting PEMWE cost competitiveness for large-scale implementation. In this study, we demonstrated, for the first time, a novel two-dimensional (2D)-patterned electrode with edge effects to address these challenges. The edge effect was induced by membrane properties, potential distribution, and counter electrode coverage and could be optimized by tuning the catalyst layer dimensions. To achieve identical PEMWE performance, the optimal pattern saved the 21% anode PGM catalyst compared with the conventional catalyst fully covered electrode. The PGM catalyst could be further reduced by 61% to boost mass activity with no significant performance loss. The results also indicated that the electrode uniformity in PEMWE cells might not be as critical as that in PEM fuel cells. Finally, the novel 2D-patterned electrode could effectively reduce PGM catalyst loading, accelerating affordable and large-scale production of hydrogen and other value-added chemicals via electrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Heavy Oil Polymer EOR in the Challenging Alaskan Arctic - It Works!

Under co-sponsorship of the US Department of Energy and Hilcorp Alaska LLC the first ever polymer field pilot commenced on 8/28/2018 in the Schrader Bluff heavy oil reservoir at the Milne Point Field on Alaska North Slope (ANS). The primary objective of the pilot is to prove the efficacy of polymer Enhanced Oil Recovery (EOR) to unlock the vast heavy oil resources on ANS. More than two and half years after startup, the polymer injection, supporting laboratory experiments and simulation studies steadily continue. The pilot started injecting hydrolyzed polyacrylamide (HPAM), at a concentration of 1,750 ppm to achieve a target viscosity of 45 cP, into the two horizontal injectors in the flood pattern. Production is monitored in the two horizontal producers. Based on laboratory measurements of polymer viscosity at reservoir conditions, the team decided to reduce polymer concentration to 1,200 ppm since July 2020 in an effort to control injection pressure and optimize polymer utilization. Quality control (QC) on the field ensures uniform polymer solution properties. Representative rock and fluid systems and test conditions are utilized in the corefloods on polymer retention, effect of injection water salinity, polymer loading, and their combinations on oil recovery. A history matched reservoir simulation model for forecasting oil recovery was developed on the basis of all the available field data. Field concerns related to the post-polymer breakthrough impact on flow assurance is addressed via specialized laboratory tests. Notwithstanding early operational disruptions and hydration issues, continuous polymer injection in both injectors has been achieved. To date, 950,000 lbs of polymer or 2 million barrels of polymer solution, equating to 13% of total pore volume (PV), has been placed in the flood pattern, serving as an effective indicator of adequate polymer injectivity. So far, the success of polymer EOR is evident from drastically reduced water cut in the producers, an estimated incremental 1,000 bopd over waterflood, and a favorable polymer utilization of 1.7 lbs/barrel of incremental oil. Polymer breakthrough was observed 26 months after the start of polymer injection. Main observations from corefloods are unusually high polymer retention values in some cases and a positive response to low salinity water. Although the heterogeneity in the flood pattern and exceptionally low water cut pose some challenges, persistent novel and justifiable simulation approaches have resulted in a robust history matched model. Experimental results on produced fluids treatment provide operational guidance for improved oil-water separation and mitigation of heater tube fouling. The ongoing success of the pilot is a key indicator of bringing the team ever closer to meeting the project’s primary objective. The encouraging results of the pilot is one of the drivers that has provided the impetus to apply polymer EOR throughout the Milne Point Field, which would increase oil recovery and extend the economic life of the Trans Alaska Pipeline System. The scientific knowledge, including the many lessons learned from this pilot also has referential value for other potential heavy oil EOR projects throughout the world. The collected data, operational lessons learned, and the overall success of the pilot are summarized in the paper.

None, None↗
DOE OSTI2026

Bipolar Membrane Electrodialyzers as Flexible Demand Response Resources: Co-Optimization of Cost Savings and Product Formation

Bipolar membrane electro dialyzers (BPMED) are widely used for chemical production and processing, including in the emerging ocean alkalinity enhancement (OAE) industry. In this paper, we explore the potential of BPMED devices as flexible electrochemical loads within power system operations. Using a multi-objective optimization framework, we evaluate BPMED operation across 24-hour and monthly horizons to examine how dispatch strategies respond to electricity price and grid conditions. Simulation results show that altering the relative weights of the choices in the objective function strongly shape the operating patterns, with cost-focused strategies that suppress the operation during peak prices. Furthermore, we propose alternative formulations that optimize operations to achieve both cost savings and alignment with periods of lower grid-side carbon intensity (CI), as low grid-side CI is key to maximize OAE efficiency. Additionally, a detailed sensitivity analysis highlights the importance of device properties, where low area-specific resistance (ASR) of membrane and high current efficiency (CE) are observed to jointly unlock cost-effective operation. However, even modest shunt efficiency losses are observed to erode performance and decrease system value. Importantly, the analysis demonstrates that BPMED can serve as a controllable and flexible demand response resource, shifting load to support multiple grid-side objectives, including (but not limited to) renewable integration, alleviate peak demand, and provide co-benefits for system reliability. These findings underscore BPMED’s dual role as a process technology and a grid-supporting asset, pointing to promising pathways for operational optimization of multiple objectives.

Bhattacharya, Saptarshi (ORCID:0000000308902060)↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

graph algorithms, high performance comptuing↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

Sattar, Naw Safrin↗
DOE OSTI2025

EVs@Scale Next-Gen Profiles - Fleet Utilization 2024

As part of the U.S. Department of Energy’s EVs@Scale initiative, the Next-Gen Profiles (NGP) project provides a comprehensive, data-driven analysis of electric vehicle (EV) and electric vehicle supply equipment (EVSE) operations across real-world fleet deployments. This paper presents findings from the NGP’s Fleet Utilization study, which investigates operational behavior and asset usage across seventeen EV fleets and two EVSE fleets, encompassing a wide range of vehicle types and use cases. Data collected from diverse sources—varying in format and temporal resolution—are first reformatted into a unified structure. From this harmonized dataset, a suite of rigorously defined performance metrics is calculated at an hourly cadence, enabling consistent cross-comparison of charging, routing, and other key operational behaviors. Amid rapidly increasing EV adoption and growing demands for energy-efficient fleet operations, the analysis reveals clear utilization trends—including diurnal and weekly activity cycles, differences in short versus long charging session dependencies, and route-specific energy usage patterns. These findings highlight the need for tailored infrastructure strategies and the deployment of advanced energy management systems, such as Distributed Energy Resource Management Systems (DERMS) and Site Energy Management Systems (SEMS), which can optimize charging schedules and mitigate peak loads. By leveraging anonymized, harmonized datasets and standardized metrics, this study offers critical insights into fleet behavior and performance, providing a foundation to improve operational efficiency, reduce costs, and enable the scalable deployment of electrified transportation.

Wells, Landon↗
DOE OSTI2018

Multimodal Nondestructive Dry Cask Basket Structure and Spent Fuel Evaluation

This NEUP-IRP assembled a team from diverse institutions and a diverse set of expertise to examine the problem of assessing the structural health of dry nuclear fuel storage casks, specifically the TN-32 design. With no physical access designed into these storage casks and the high cost and risk associated with opening casks for direct inspection, the technical difficulty of structural assessment of the basket structure and fuel assemblies is high. In response to this challenge, this IRP brought together experts in both traditional and novel NDE methodologies: active acoustics (University of Mississippi), emissions tomography (University of Florida), muon tomography (Oregon State University), and passive-active ultrasonics (University of South Carolina). Further, our industry partners in the Electric Power Research Institute (EPRI) and AREVA provided invaluable insight into technical design details for the cask, practical knowledge of operating parameters at a storage facility, and key relationships that enabled activities unforeseen at the time of the proposed work but were critical in guiding the path of the research. The team decided early in the project that we had a collective goal of laying the technical ground work for practical solutions that could be implemented in the field with reasonable cost, time, and reliability. A fortunate opportunity arose early in this IRP project to obtain access to a new TN-32 cask at Columbiana High Tech. Both acoustics teams were given access for testing concepts in the first year of the project. This ended up providing critical guidance in the design of subsequent laboratory experiments. Specifically, it became clear that the only reliable acoustic pathways to the interior were through the trunnions, base plate, and top ring of the cask. Based on these field tests and discussions about what would be of practical interest, the team as a whole decided a realistic goal was to detect catastrophic failure of a single fuel assembly, or one that was missing. It is clear from this work that the active acoustics / resonance and muon tomography methodologies would be capable of detecting interior structural integrity problems at this level. The other two areas of investigation (emissions tomography and passive/active ultrasonics) would need more testing to determine a successful implementation, but each may also have other applications such as long term monitoring and monitoring during transportation. This IRP project resulted in technical advances with applications that extend beyond the problem at hand, supported the training of 10 graduate students + 1 post doc, 21 publications, and 13 conference presentations. The remainder of this summary highlights significant activities and achievements in each of the methodologies and a brief discussion on how they can be combined to leverage strengths. Active Acoustics The active acoustics team at UM used both linear and non-linear structural resonance methods to infer information about the internal structure of a cask. Significant activities were a set of four field test measurements on a full scale TN-32 cask at the Columbiana Hi Tech facility in Greensboro, NC and the design and fabrication of a 1/6th scale model TN-32 cask for laboratory measurements. A significant set of acoustic data has been acquired on both the full scale and laboratory scale casks. It was determined early in the project that mechanical resonance techniques were not going to be sensitive to small scale (centimeter) cracks and structural defects in the basket structure or fuel assemblies. We were able to ascertain that both the energy and complexity of the resonance spectra were sensitive to “bulk state” of the fuel assemblies – an intact assembly versus a severely degraded assembly. By developing a novel combination of metrics, we are able to measure a statistically significant shift due to one out of the 32 fuel assemblies being either missing or degraded to a point of complete loss of structural coherence (i.e. “rubble”). We believe this combination of resonance metrics is new to the field and will have applications beyond the fuel storage problem. Emissions Tomography The Emissions Tomography team at the University of Florida explored the concept of using scattering of radiation emissions from the stored fuel as method of evaluating the internal structural health of the cask. Due to the need for emissive fuel assemblies and the difficulties of gaining access to storage facilities, this team primarily relied on simulations to inform feasibility. A key question was the flux estimates of neutrons with a small enough number of scattering events to retain spatial information. Some key conclusions from the transmission component of this work are that the number of scattering events falls rapidly with energy – setting a 11MeV threshold, the average number of scattering events is 8∓4. Rough estimates of scanning times using an ideal detector array would be 1 week. The team also explored the case of significant structural failure resulting in a shift of the source material (fuel). The result is that a +/- 2% mass shift results in a detectable (5.13%) shift in the correlated 2 neutron emission counts and thus may be a candidate as an initial survey. Finally, simulations for predicting the azimuthal dependence of the flux based on specific loading patterns were conducted. Due to self-shielding effects of the assemblies, a periodic pattern results and could potentially be used to identify missing or severely degraded assemblies. This work forms a basis for proper detector placement with the goal of optimizing sensitivity. Muon Tomography Muon tomography (MT) is a relatively novel method of interrogating interior structure on a large scale. One of the advantages of this technique is that the exciting energy source (cosmic muons) is ubiquitous and relatively stable in time. Challenging aspects are relatively low flux rates and appropriate detector technologies. The efforts of the muon group at Oregon State University focused on two areas: (1) simulation of various combinations of muon scattering events and detection schemes to assess sensitivities and time scales for image construction and (2) building a prototype muon detection system applicable to the dry storage cask problem as well as many other similar systems. Significant accomplishments in this area are: (1) demonstration that MT is a viable method for detecting missing or severely degraded rod assemblies, (2) a scintillation based detector with 4 panels and 32 channels of position data each were constructed, tested, and calibrated. Testing of the panels using natural muon background and no scattering objects yielded largely expected results – a straight line path for the muon through the four panels. However some anomalies (paths deviating from a line) were also detected. A redesign of the board improved the signal which greatly improved the muon tracking through the panels. Testing with lead bricks as scattering bodies showed a reduction in the coincidence rate with increasing bricks indicating increased scattering events. A refinement of the scintillator detector design resulted in a spatial image resolution of around 2 cm based on lead brick spacing tests lasting 24 hours. Passive/Active Ultrasonics This team at the University of South Carolina investigated the feasibility of combining passive ultrasonics, in which an elastic pulse is generated by a degradation process (such as a crack extending), and active ultrasonics, in which a known excitation is introduced into the structure and detected by an array of transducers. Significant accomplishments in this area include the development of elastic wave propagation simulation in relevant materials, development of diagnostic algorithms, and laboratory testing and validation using laser doppler vibrometry. This group demonstrated the applicability of various non-contact vibration detection methodologies such as air-coupled ultrasonic transducers and scanning laser doppler vibrometry. While passive acoustic emissions monitoring will likely have a role during the transportation of a fuel storage cask, the level of detailed information about the defect generating the event which can be ascertained from the detections in a complex structure remains for future work. Thermography The EPRI team contributed early in the project a series of thermography measurements in which various levels of water pooled in a mock-up canister were analyzed using commercially available infrared imaging technologies. While large levels of water (100 L, ~10 cm deep) were observable in the thermograms, more realistic levels of pooled water (1 L) were not detectable whether localized or distributed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

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↗
DOE OSTI2017

Literature Survey on Operational Voltage Control and Reactive Power Management on Transmission and Sub-Transmission Networks

Voltage and reactive power system control is generally performed following usual patterns of loads, based on off-line studies for daily and seasonal operations. This practice is currently challenged by the inclusion of distributed renewable generation, such as solar. There has been focus on resolving this problem at the distribution level; however, the transmission and sub-transmission levels have received less attention. This paper provides a literature review of proposed methods and solution approaches to coordinate and optimize voltage control and reactive power management, with an emphasis on applications at transmission and sub-transmission level. The conclusion drawn from the survey is that additional research is needed in the areas of optimizing switch shunt actions and coordinating all available resources to deal with uncertain patterns from increasing distributed renewable generation in the operational time frame. These topics are not deeply explored in the literature.

capacitors↗
DOE OSTI2020

Distributed Solution Approach for a Stackelberg Pricing Game of Aggregated Demand Response

Demand-side management is a fundamental up-to-date strategy that transforms the traditional power grid to a modern smart grid where the flexible pricing mechanisms play a critical role in its successful implementation. In this paper, the pricing-demand response between a distribution system operator (DSO) and load aggregators (LAs) is modeled as a Stackelberg game, where the DSO is the price maker that adjusts its strategy based on observed responses from LAs. With the concerns of computational cost and privacy protection, two distributed solution approaches, particle swarm optimization and pattern search algorithm, are conducted and compared with the classical centralized backward induction approach. Numerical results on a small case study demonstrate the effectiveness of the proposed distributed solution approaches in leveraging flexible demand response potential.

Chen, Yang↗
DOE OSTI2022

Enhancing scalability of a matrix-free eigensolver for studying many-body localization

We propose several techniques to enhance the parallel scalability of a matrix-free eigensolver designed for studying many-body localization (MBL) of quantum spin chain models with nearest-neighbor interactions and on-site disorder. This type of problem is computationally challenging because the dimension of the associated Hamiltonian matrix grows exponentially with respect to the number of spins L, and we need to average over different realizations of the random disorder to obtain relevant statistical behavior. For each disorder realization, we need to compute eigenvalues from different regions of the spectrum and their corresponding eigenvectors. In previous work, the interior eigenstates for a single eigenvalue problem are computed via the shift-and-invert Lanczos algorithm. Due to the extremely high memory footprint of the LU factorizations, this technique is not well suited for large L’s. For example, we need thousands of compute nodes on modern high performance computing infrastructures to go beyond L = 24. The matrix-free approach does not suffer from this memory bottleneck, however, its scalability is limited by a computation and communication load imbalance. To reduce this imbalance and to significantly enhance the scalability of the matrix-free eigensolver, we reorder the matrix and leverage the consistent space runtime, CSPACER. We also show its efficiency in managing irregular communication patterns at scale compared to optimized MPI non-blocking two-sided and one-sided RMA implementation variants. This effort enables us to study MBL for spin chains with a larger number of spins. The efficiency and effectiveness of the proposed algorithm is demonstrated by computing eigenstates on a massively parallel many-core high performance computer.

METIS↗
NASA NTRS2006

Development of a Countermeasure to Enhance Postflight Locomotor Adaptability

Astronauts returning from space flight experience locomotor dysfunction following their return to Earth. Our laboratory is currently developing a gait adaptability training program that is designed to facilitate recovery of locomotor function following a return to a gravitational environment. The training program exploits the ability of the sensorimotor system to generalize from exposure to multiple adaptive challenges during training so that the gait control system essentially learns to learn and therefore can reorganize more rapidly when faced with a novel adaptive challenge. We have previously confirmed that subjects participating in adaptive generalization training programs using a variety of visuomotor distortions can enhance their ability to adapt to a novel sensorimotor environment. Importantly, this increased adaptability was retained even one month after completion of the training period. Adaptive generalization has been observed in a variety of other tasks requiring sensorimotor transformations including manual control tasks and reaching (Bock et al., 2001, Seidler, 2003) and obstacle avoidance during walking (Lam and Dietz, 2004). Taken together, the evidence suggests that a training regimen exposing crewmembers to variation in locomotor conditions, with repeated transitions among states, may enhance their ability to learn how to reassemble appropriate locomotor patterns upon return from microgravity. We believe exposure to this type of training will extend crewmembers locomotor behavioral repertoires, facilitating the return of functional mobility after long duration space flight. Our proposed training protocol will compel subjects to develop new behavioral solutions under varying sensorimotor demands. Over time subjects will learn to create appropriate locomotor solution more rapidly enabling acquisition of mobility sooner after long-duration space flight. Our laboratory is currently developing adaptive generalization training procedures and the associated flight hardware to implement such a training program during regular inflight treadmill operations. A visual display system will provide variation in visual flow patterns during treadmill exercise. Crewmembers will be exposed to a virtual scene that can translate and rotate in six-degrees-of freedom during their regular treadmill exercise period. Associated ground based studies are focused on determining optimal combinations of sensory manipulations (visual flow, body loading and support surface variation) and training schedules that will produce the greatest potential for adaptive flexibility in gait function during exposure to challenging and novel environments. An overview of our progress in these areas will be discussed during the presentation.

Bloomberg, Jacob J.↗
DOE OSTI2017

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Restoration Strategy for Active Distribution Systems Considering Endogenous Uncertainty in Cold Load Pickup

Cold load pickup (CLPU) phenomenon is identified as the persistent power inrush upon a sudden load pickup after an outage. Under the active distribution system (ADS) paradigm, where distributed energy resources (DERs) are extensively installed, the decreased outage duration can induce a strong interdependence between CLPU pattern and load pickup decisions. In this paper, we propose a novel modelling technique to tractably capture the decision-dependent uncertainty (DDU) inherent in the CLPU process. Subsequently, a two-stage stochastic decision-dependent service restoration (SDDSR) model is constructed, where first stage searches for the optimal switching sequences to decide step-wise network topology, and the second stage optimizes the detailed generation schedule of DERs as well as the energization of switchable loads. Further, to tackle the computational burdens introduced by mixed-integer recourse, the progressive hedging algorithm (PHA) is utilized to decompose the original model into scenario-wise subproblems that can be solved in parallel. The numerical test on modified IEEE 123-node test feeders has verified the efficiency of our proposed SDDSR model and provided fresh insights into the monetary and secure values of DDU quantification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput

The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. Here, by reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.

AWS↗