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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Sparse Deep Neural Network Inference using different Programming Models

Sparse deep neural networks have gained increasing attention recently in achieving speedups on inference with reduced memory footprints. However, the real-world applications are little shown with specialized optimizations, yet a wide variety of DNN tasks remain dense without exploiting the advantages of sparsity in networks. Recent work presented by MIT/IEEE/Amazon GraphChallenge has demonstrated significant speedups and various techniques. Still, we find that there is limited investigation of the impact of various Python and C\slash C++ based programming models to explore new opportunities in general cases. In this work, we provide performance evaluation through different programming models using CuPy, cuSPARSE, and OpenMP to discuss the advantages and disadvantages of our sparse implementations on single-GPU and multiple GPUs of NVIDIA DGX-A100 40GB/80GB platforms.

machine learning, HPC↗

DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm

This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2016

SU-F-R-28: Correction of FCh-PET Bladder Uptake Using Virtual Sinograms and Investigation of Its Impact On the Quantification of Prostate Textural Characteristics

Purpose: Textural biomarkers as a tool for quantifying intratumoral heterogeneity hold great promise for diagnosis and early assessment of treatment response in prostate cancer. However, spill-in counts from the bladder uptake are suspected to have an impact on the textural measurements of the prostate volume. This work proposes a correction method for the FCh-PET bladder uptake and investigates its impact on intraprostatic textural properties. Methods: Two patients with PC received pre-treatment dynamic FCh-PET scans reconstructed at four time points (interval: 2 min), for which prostate and bladder contours were obtained. Projection bins affected by bladder uptake were determined by forward-projection. For each time point and axial position, virtual sinograms were obtained and affected bins replaced by a weighted combination of original values and values interpolated using cubic spline from non-affected bins of the current and adjacent projection angles. The process was optimized using a genetic algorithm in terms of minimization of the root-mean-square error (RMSE) within the bladder between the corrected dynamic time point volume and a reference initial uptake volume. Finally, the impact of the bladder uptake correction on the prostate region was investigated using two standard SUV metrics (1) and three texture metrics (2): 1) SUVmax, SUVmean; 2) Contrast, Homogeneity, Coarseness. Results: Without bladder uptake correction, SUVmax and SUVmean were on average overestimated in the prostate by 0%, 0%, 33.2%, 51.2%, and 3.6%, 6.0%, 2.9%, 3.2%, for each time point respectively. Contrast varied by −9.1%, −6.7%, +40.4%, +107.7%, and Homogeneity and Coarseness by +4.5%, +1.8%, −8.8%, −14.8% and +1.0%, +0.5%, −9.5%, +0.9%. Conclusion: We proposed a method for FCh-PET bladder uptake correction and showed an impact on the quantification of the prostate signal. This method achieved a large reduction of intra-prostatic SUVmax while minimizing the impact on SUVmean. Further investigation is necessary to interpret changes in textural features. SL acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290).

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Autonomous Fracture Conductivity Using Expandable Proppants in Enhanced Geothermal Systems

Summary Early thermal breakthrough in enhanced geothermal systems (EGS) due to the presence of preferential flow channels is a major challenge that endangers efficient and economic heat extraction in such systems. Previous studies mainly focused on adjusting circulation rates of the working fluid, which still leaves significant amounts of untapped heat behind. Currently, there is a lack of technologies for altering flow distribution within the fracture network to achieve uniform heat sweeping in the reservoir. This work presents a novel concept for making proppants to autonomously control fracture conductivity based on the surrounding temperature. Here, proppants with negative thermal expansion coefficients have demonstrated the capability for appropriate fracture conductivity adjustment as a function of temperature to achieve uniform flow across the fracture network. Particle-particle interactions governing such functions are explicitly modeled, and then the Lattice Boltzmann methods (LBM) is used to determine the potential impact of closure stress and temperature changes on the permeability of the proposed proppant packs. Microscale analyses are further used to determine the required material properties to achieve a certain improvement in the permeability of the proppant pack. Our analyses show an enhancement in permeability and the associated fracture conductivity by half of their initial values. Field-scale analysis further confirms the effectiveness of the proposed concept as 31.4% more heat can be extracted from EGS over 50 years of production when the proposed proppants are used. Such novel proppants may effectively delay thermal breakthrough, sweep heat from larger rock volumes, and elongate the life span of the EGS project.

Engineering↗

DOE OSTI2024

Evolving Multi-hazard Machine Learning Modeling for Advanced Risk-Informed Infrastructure Resilience Assessment

The socioeconomic impacts of pipeline incidents have escalated over the past three decades, revealing the limitation of traditional risk modeling methods when applied to extensive pipeline networks. This research aims to develop machine learning (ML) models that effectively identify, rank, and predict the diverse hazards and socioeconomic consequences associated with pipeline incidents. Utilizing historical data on pipeline incidents alongside weather and oceanographic data from the 1980s onward, the Houston metropolitan area serves as a testbed for the proposed methodologies. The research segments the combined datasets into three consecutive periods, demonstrating the efficacy of the updated model in predicting future events, particularly concerning precipitation rate data. Despite the challenges posed by a relatively limited dataset, local-level ML modeling offers valuable insights into the spatial and temporal dynamics of multiple hazards that contribute to pipeline incidents. These findings hold significant implications for future research, particularly in understanding and mitigating risks in various locations across the Gulf Coast and other coastal regions.

42 ENGINEERING↗

DOE OSTI2015

Species interactions differ in their genetic robustness

Conflict and cooperation between bacterial species drive the composition and function of microbial communities. Stability of these emergent properties will be influenced by the degree to which species' interactions are robust to genetic perturbations. We use genome-scale metabolic modeling to computationally analyze the impact of genetic changes when Escherichia coli and Salmonella enterica compete, or cooperate. We systematically knocked out in silico each reaction in the metabolic network of E. coli to construct all 2583 mutant stoichiometric models. Then, using a recently developed multi-scale computational framework, we simulated the growth of each mutant E. coli in the presence of S. enterica . The type of interaction between species was set by modulating the initial metabolites present in the environment. We found that the community was most robust to genetic perturbations when the organisms were cooperating. Species ratios were more stable in the cooperative community, and community biomass had equal variance in the two contexts. Additionally, the number of mutations that have a substantial effect is lower when the species cooperate than when they are competing. In contrast, when mutations were added to the S. enterica network the system was more robust when the bacteria were competing. These results highlight the utility of connecting metabolic mechanisms and studies of ecological stability. Cooperation and conflict alter the connection between genetic changes and properties that emerge at higher levels of biological organization.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Digital Twin Applications in the Water Sector: A Review

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure.

digital twin↗

NASA NTRS1973

Velocity structure and evolution of the moon

Seismic data from the Apollo Passive Seismic Network stations are analyzed to determine the velocity structure and to infer the composition and physical properties of the lunar interior. Data from artificial impacts (SIBV booster and LM-ascent stage) cover a distance range of 9 to 1750 km. Travel times and amplitudes, as well as theoretical seismograms, are used to derive a velocity model for the outer 150 km of the moon. The P-wave velocity model confirms an earlier report of a lunar crust in the eastern part of Oceanus Procellarum. The crust is about 60 km thick and may consist of two layers in the mare regions. Possible values for the P-wave velocity in the uppermost mantle are between 7.6 and 9.0 km/sec. The 9 km/sec velocity represents either a localized heterogeneous unit, or a thin layer less than about 40 km in thickness. The elastic properties of the deep interior, as inferred from the seismograms of natural events (meteoroid impacts and moonquakes) occurring at great distances, indicate that there is an increase in attenuation and a possible decrease of velocity at depths below about 1000 km.

Toksoz, M. N.↗

NASA NTRS1983

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

Kratochvil, D.↗

NASA NTRS1991

Transforming AdaPT to Ada

This paper describes how the main features of the proposed Ada language extensions intended to support distribution, and offered as possible solutions for Ada9X can be implemented by transformation into standard Ada83. We start by summarizing the features proposed in a paper (Gargaro et al, 1990) which constitutes the definition of the extensions. For convenience we have called the language in its modified form AdaPT which might be interpreted as Ada with partitions. These features were carefully chosen to provide support for the construction of executable modules for execution in nodes of a network of loosely coupled computers, but flexibly configurable for different network architectures and for recovery following failure, or adapting to mode changes. The intention in their design was to provide extensions which would not impact adversely on the normal use of Ada, and would fit well in style and feel with the existing standard. We begin by summarizing the features introduced in AdaPT.

Goldsack, Stephen J.↗

NASA NTRS2004

VLBI2010: Networks and Observing Strategies

The Observing Strategies Sub-group of IVS's Working Group 3 has been tasked with producing a vision for the following aspects of geodetic VLBI: antenna-network structure and observing strategies; source strength/structure/distribution; frequency bands, RFI; and field system and scheduling. These are high level considerations that have far reaching impact since they significantly influence performance potential and also constrain requirements for a number of other \VG3 sub-groups. The paper will present the status of the sub-group's work on these topics.

Petrachenko, Bill↗

NASA NTRS2020

Characterizing 15 Years of Saharan-like, Dry, Well-Mixed Air Layers in North Africa

The Saharan Air Layer (SAL) is a dry, well-mixed layer (WML) of warm and sometimes dusty air of nearly constant water vapor mixing ratio generated by the intense surface heating and strong, dry convection in the Sahara Desert, which has notable downstream impacts on the surface energy balance, organized convective system development, seasonal precipitation, and air quality. Characterizing both WMLs and SALs from the existing rawinsonde network has proven challenging because of its sparseness and inconsistent data reporting. Spurred on by this challenge, we previously created a detection methodology and supporting software to automate the identification and characterization of WMLs from multiple data sources including rawinsondes, remote sensing platforms, and model products. We applied our algorithm to each dataset at both its native and at a common (most coarse data product) vertical resolution to detect WMLs and their characteristics (temperature, mixing ratio, AOD, etc.) at each of the 53 rawinsonde launch sites in north Africa.

Nicholls, Stephen D.↗

NASA NTRS2024

Sensor Fault Detection in Smart Extraterrestrial Habitats Using Unsupervised Learning

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature–pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the neural network is investigated and demonstrated. The capabilities and limitations of the proposed solution are discussed.

Zixin Wang↗

DOE OSTI2023

Machine Learning Based Fracture Network Quantification at the IBDP CO2 Sequestration Site

This poster presents the latest results highlighted in SMART phase II task 4 of fracture network and mapping based on IBDP field data measurement. The results will be coupled into the dynamic modeling and simulation study to insight the impact of the fracture in the reservoir system. Such impacts will be applied to AI/ML development for training and test for science-informed decision making support for CO2 storage.

Kumar, Abhash↗

NASA NTRS2012

Using Total Lightning Observations to Enhance Lightning Safety

Lightning is often the underrated threat faced by the public when it comes to dangerous weather phenomena. Typically, larger scale events such as floods, hurricanes, and tornadoes receive the vast majority of attention by both the general population and the media. This comes from the fact that these phenomena are large, longer lasting, can impact a large swath of society at one time, and are dangerous events. The threat of lightning is far more isolated on a case by case basis, although millions of cloud-to-ground lightning strikes hit this United States each year. While attention is given to larger meteorological events, lightning is the second leading cause of weather related deaths in the United States. This information raises the question of what steps can be taken to improve lightning safety. Already, the meteorological community s understanding of lightning has increased over the last 20 years. Lightning safety is now better addressed with the National Weather Service s access to the National Lightning Detection Network data and enhanced wording in their severe weather warnings. Also, local groups and organizations are working to improve public awareness of lightning safety with easy phrases to remember, such as "When Thunder Roars, Go Indoors." The impacts can be seen in the greater array of contingency plans, from airports to sports stadiums, addressing the threat of lightning. Improvements can still be made and newer technologies may offer new tools as we look towards the future. One of these tools is a network of sensors called a lightning mapping array (LMA). Several of these networks exist across the United States. NASA s Short-term Prediction Research and Transition Center (SPoRT), part of the Marshall Spaceflight Center, has access to three of these networks from Huntsville, Alabama, the Kennedy Space Center, and Washington D.C. The SPoRT program s mission is to help transition unique products and observations into the operational forecast environment. SPoRT has been collaborating with the Huntsville National Weather Service (NWS) Office since 2003 and has since included several other offices to better implement LMA observations into real-time applications. Much of that work has focused on the LMA s ability to detect intra-cloud lightning in addition to cloud-to-ground lightning strikes. Combined, these observations are called total lightning. With total lightning observations, NWS offices can enhance their situational awareness and improve severe weather warnings. Just as importantly, the observed intra-cloud flashes often precede the first cloud-to-ground strike by a few minutes. SPoRT and its partner NWS offices are working to develop visualizations and applications to better utilize these data. However, there is a drawback. The LMAs have a short range of no more than 200 km. This is being addressed with the next generation geostationary satellite, GOES-R, which will boast the Geostationary Lightning Mapper (GLM). SPoRT, in conjunction with NOAA s GOES-R Proving Ground, is working to prepare the end user community for the GLM era using the LMA observations as a demonstration tool. Working collaboratively with our NWS partners, SPoRT is working to determine how best to integrate these future observations to improve both severe storm warnings and lightning safety.

Stano, Geoffrey T.↗

DOE OSTI2024

Advancing earth system model calibration: a diffusion-based method

Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.

Liu, Yanfang↗

DOE OSTI2024

3D Quantification of Pore Networks and Anthropogenic Carbon Mineralization in Stacked Basalt Reservoirs

Basalt formations are promising candidates for the geologic storage of anthropogenic CO 2 due to their storage capacity, porosity, permeability, and reactive geochemical trapping ability. The Wallula Basalt Carbon Storage Pilot Project demonstrated that supercritical CO 2 injected into >800 m deep Columbia River Basalt Group stacked reservoir flow tops mineralizes to ankerite-siderite-aragonite on month-year timescales, with 60% of the 977 metric tonnes of CO 2 converted within two years. The potential impacts of mineral precipitation and consequent changes on rock porosity, pore structure, pore size, and pore size distributions have likely been underestimated hitherto. Herein, we address these knowledge gaps using X-ray Micro Computed Tomography (XMT) to evaluate the pore network architecture of sidewall cores recovered two years after CO 2 injection. In the present study, we performed a detailed quantitative analysis of the CO 2 -reacted basalt cores by XMT imaging. Reconstructed 3D images were analyzed to study the distribution and volumetric details of porosity and carbonate nodules in the cores along with the various other phases, providing insight into paragenesis and carbonate growth mechanisms, including mineralogic/chemical zonation. Finally, these findings are being used to parametrize multiphase reactive transport models to predict the fate and transport of subsurface CO 2 , enabling scale-up to commercial-scale geologic carbon storage in basalts and other reactive mafic-ultramafic formations.

54 ENVIRONMENTAL SCIENCES↗