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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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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Accurate Prediction of Algal Biomass Lipid, Protein, and Carbohydrate Composition with Machine Learning Regression Modelling of Near-IR Spectra

During large scale algal biomass cultivation, it is difficult to reliably control relative composition to target levels. Rapid determination of chemical composition is feasible by using near infrared (NIR) spectral data. We sought to build and improve on reliable high-throughput screening prediction method based on partial least squares regression (PLSR) by the application of artificial neural networks (ANN) and associated optimization strategies. The algal biomass sample set was designed and created in an iterative process of culturing in physiologically diverse conditions at the GAI field site, followed by compositional analyses at NREL. The workflow allowed us to identify gaps in compositional space for informing the subsequent cultivation and sampling efforts and generated a high quality set of 210 unique samples with chemical analysis results, spectral scanning data, and cultivation metadata. We observed a significant improvement in the performance of carbohydrate content predictions using an optimized ANN model compared to PLSR, with > 16% reduction in mean absolute percent error (MAPE) when tested on the same set of reserved data. The optimized ANN models for FAME and protein prediction performed exceptionally well with 5.99% and 5.09% MAPE, respectively. Application of these methods to detection and quantification of minor biomass constituents that are relevant to certain product streams has shown positive preliminary results, opening the possibility for extensions to the outputs of this powerful data type. All models are accompanied by prediction uncertainties and unsupervised spectral outlier detection to alert an operator to unreliable spectral data. These tools can be deployed for rapid determination of algal culture status, and cultivation and biomass quality improvement.

algal biofuels↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Mapping Glacier Basal Sliding Applying Machine Learning

During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on-ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.

58 GEOSCIENCES↗
DOE OSTI2022

MPC solution for optimal load shifting for buildings with ON/OFF staged packaged units: Experimental demonstration, and lessons learned

Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. In this work, two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2016

Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification

Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.

Nguyen, Nhan T.↗
DOE OSTI2024

Relevant biochar characteristics influencing compressive strength of biochar-cement mortars

To counteract the contribution of CO 2 emissions by cement production and utilization, biochar is being harnessed as a carbon-negative additive in concrete. Increasing the cement replacement and biochar dosage will increase the carbon offset, but there is large variability in methods being used and many researchers report strength decreases at cement replacements beyond 5%. This work presents a reliable method to replace 10% of the cement mass with a vast selection of biochars without decreasing ultimate compressive strength, and in many cases significantly improving it. By carefully quantifying the physical and chemical properties of each biochar used, machine learning algorithms were used to elucidate the three most influential biochar characteristics that control mortar strength: initial saturation percentage, oxygen-to-carbon ratio, and soluble silicon. These results provide additional research avenues for utilizing several potential biomass waste streams to increase the biochar dosage in cement mixes without decreasing mechanical properties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Safety and Management Framework to Enable Automated Mobility Districts in Urban Areas

Automated mobility technology is beginning to emerge as a viable means to create sustainable and effective public mobility systems within denser urban environments. Automated mobility districts (AMDs) describe major urban districts or activity centers in which deployments of multiple automated vehicle (AV) transit and ride-hailing fleets are supported to meet public mobility needs. The authors put forward a framework to enable AMDs and their governing and management jurisdictional authorities to manage safety of AV operations based on lessons learned from the last century of automated guideway transit and roadway intersection traffic control systems. The essential concept is that of operational management and safety-critical control of multiple AV fleets using a “system-of-systems” approach to system safety analysis. The safety analysis would focus on safe passage of the AV fleet vehicles through complex roadway intersections and junctions, especially in the presence of other non-automated modes such as pedestrians and manually operated vehicles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Engineering advancements in microfluidic systems for enhanced mixing at low Reynolds numbers

Mixing within micro- and millichannels is a pivotal element across various applications, ranging from chemical synthesis to biomedical diagnostics and environmental monitoring. The inherent low Reynolds number flow in these channels often results in a parabolic velocity profile, leading to a broad residence time distribution. Achieving efficient mixing at such small scales presents unique challenges and opportunities. This review encompasses various techniques and strategies to evaluate and enhance mixing efficiency in these confined environments. It explores the significance of mixing in micro- and millichannels, highlighting its relevance for enhanced reaction kinetics, homogeneity in mixed fluids, and analytical accuracy. We discuss various mixing methodologies that have been employed to get a narrower residence time distribution. The role of channel geometry, flow conditions, and mixing mechanisms in influencing the mixing performance are also discussed. Various emerging technologies and advancements in microfluidic devices and tools specifically designed to enhance mixing efficiency are highlighted. We emphasize the potential applications of micro- and millichannels in fields of nanoparticle synthesis, which can be utilized for biological applications. Additionally, the prospects of machine learning and artificial intelligence are offered toward incorporating better mixing to achieve precise control over nanoparticle synthesis, ultimately enhancing the potential for applications in these miniature fluidic systems.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to inject sufficient resiliency into the operations of the network of microgrids. We consider adversarial actions to the voltage and power control loop reference signals at the grid forming (GFM) inverters in the microgrids which are essential to integrate renewable resources. Therefore, we formulate a resilient reinforcement learning training setup that uses these adversarial injections to generate episodic trajectories and train the RL agents to alleviate their impact on performance. To circumvent the concerns about data-sharing and privacy for different owners of the microgrids in the networked setting, we bring in the aspects of the federated operation to propose novel Fed-RL algorithms. As the dynamics of each microgrid are coupled due to electrical interlinks, the conventional federated RL approaches using decoupled independent environments are not applicable, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC). We have performed numerical simulations on an IEEE 123-bus benchmark test feeder with three microgrids by creating a customized simulation setup by encapsulating the microgrid dynamic simulations in GridLAB-D/HELICS co-simulation platform with the OpenAI Gym environment and validated the proposed resilient and secured learning methodology.

Artificial Intelligence (AI), reinforcement learni↗
DOE OSTI2019

Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids

Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This work develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions and Gaussian injections in particular. Furthermore, our algorithm applies to the practical case of unbalanced three-phase power flow. The performance is validated on AC power flow simulations over three phase IEEE distribution grid test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

TU-C-201-00: Clinical Implementation of HDR Brachytherapy

Recent use of HDR has increased while planning has become more complex often necessitating 3D image-based planning. While many guidelines for the use of HDR exist, they have not kept pace with the increased complexity of 3D image-based planning. Furthermore, no comprehensive document exists to describe the wide variety of current HDR clinical indications. This educational session aims to summarize existing national and international guidelines for the safe implementation of an HDR program. A summary of HDR afterloaders available on the market and their existing applicators will be provided, with guidance on how to select the best fit for each institution’s needs. Finally, the use of checklists will be discussed as a means to implement a safe and efficient HDR program and as a method by which to verify the quality of an existing HDR program. This session will provide the perspective of expert HDR physicists as well as the perspective of a new HDR user. Learning Objectives: Summarize national and international safety and staffing guidelines for HDR implementation Discuss the process of afterloader and applicator selection for gynecologic, prostate, breast, interstitial, surface treatments Learn about the use of an audit checklist tool to measure of quality control of a new or existing HDR program Describe the evolving use of checklists within an HDR program.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-C-201-01: Clinical Implementation of HDR: A New User’s Perspective

Recent use of HDR has increased while planning has become more complex often necessitating 3D image-based planning. While many guidelines for the use of HDR exist, they have not kept pace with the increased complexity of 3D image-based planning. Furthermore, no comprehensive document exists to describe the wide variety of current HDR clinical indications. This educational session aims to summarize existing national and international guidelines for the safe implementation of an HDR program. A summary of HDR afterloaders available on the market and their existing applicators will be provided, with guidance on how to select the best fit for each institution’s needs. Finally, the use of checklists will be discussed as a means to implement a safe and efficient HDR program and as a method by which to verify the quality of an existing HDR program. This session will provide the perspective of expert HDR physicists as well as the perspective of a new HDR user. Learning Objectives: Summarize national and international safety and staffing guidelines for HDR implementation Discuss the process of afterloader and applicator selection for gynecologic, prostate, breast, interstitial, surface treatments Learn about the use of an audit checklist tool to measure of quality control of a new or existing HDR program Describe the evolving use of checklists within an HDR program.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-C-201-02: Clinical Implementation of HDR: Afterloader and Applicator Selection

Recent use of HDR has increased while planning has become more complex often necessitating 3D image-based planning. While many guidelines for the use of HDR exist, they have not kept pace with the increased complexity of 3D image-based planning. Furthermore, no comprehensive document exists to describe the wide variety of current HDR clinical indications. This educational session aims to summarize existing national and international guidelines for the safe implementation of an HDR program. A summary of HDR afterloaders available on the market and their existing applicators will be provided, with guidance on how to select the best fit for each institution’s needs. Finally, the use of checklists will be discussed as a means to implement a safe and efficient HDR program and as a method by which to verify the quality of an existing HDR program. This session will provide the perspective of expert HDR physicists as well as the perspective of a new HDR user. Learning Objectives: Summarize national and international safety and staffing guidelines for HDR implementation Discuss the process of afterloader and applicator selection for gynecologic, prostate, breast, interstitial, surface treatments Learn about the use of an audit checklist tool to measure of quality control of a new or existing HDR program Describe the evolving use of checklists within an HDR program.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-C-201-03: The Use of Checklists and Audit Tools for Safety and QA

Recent use of HDR has increased while planning has become more complex often necessitating 3D image-based planning. While many guidelines for the use of HDR exist, they have not kept pace with the increased complexity of 3D image-based planning. Furthermore, no comprehensive document exists to describe the wide variety of current HDR clinical indications. This educational session aims to summarize existing national and international guidelines for the safe implementation of an HDR program. A summary of HDR afterloaders available on the market and their existing applicators will be provided, with guidance on how to select the best fit for each institution’s needs. Finally, the use of checklists will be discussed as a means to implement a safe and efficient HDR program and as a method by which to verify the quality of an existing HDR program. This session will provide the perspective of expert HDR physicists as well as the perspective of a new HDR user. Learning Objectives: Summarize national and international safety and staffing guidelines for HDR implementation Discuss the process of afterloader and applicator selection for gynecologic, prostate, breast, interstitial, surface treatments Learn about the use of an audit checklist tool to measure of quality control of a new or existing HDR program Describe the evolving use of checklists within an HDR program.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

First Ever Polymer Flood Field Pilot to Enhance the Recovery of Heavy Oils on Alaska North Slope – Producer Responses and Operational Lessons Learned

The US Department of Energy and Hilcorp Alaska, LLC are jointly cosponsoring a field pilot project entitled “First Ever Field Pilot on Alaska’s North Slope to Validate the Use of Polymer Floods for Heavy Oil Enhanced Oil Recovery (EOR).” The objective of this pilot is to perform a field experiment to validate the use of an advanced polymer flooding technology to unlock the vast heavy oil resources on Alaska North Slope (ANS). Two pairs of horizontal injection and production wells have been deployed in an isolated fault block of the Schrader Bluff heavy oil reservoir at the Milne Point Field to conduct a polymer flood pilot which was designed to acquire field performance data to optimize polymer flood design in the Schrader Bluff heavy oil reservoirs on ANS. Hydrolyzed polyacrylamide (HPAM) polymer injection started on August 28, 2018 using a custom made polymer mixing and pumping unit. Polymer solution properties such as concentration, viscosity, and filter ratio, have been closely monitored and adjusted as necessary. Operational procedures have been improved to ensure adequate injectivity and polymer propagation through the formation. Oil and water production is tested frequently to monitor water cut performance, and produced water is sampled and analyzed for polymer content. Tracers were pumped pre and post polymer injection startup to compare polymer flood breakthrough timing and sweep efficiency. Polymer injectivity of the horizontal wells drilled in the Schrader Bluff NB sand is found to be sufficient to replace the reservoir production voidage although there were some declines as high viscosity polymer swept the near wellbore region. Production data show significant reduction in water cut and the oil production rate has stabilized. No polymer production has been confirmed from the two horizontal producers after 22 months of polymer injection into the two supporting horizontal injectors. Tracer data show very low sweep efficiencies with waterflood alone, whereas significant improvement has been achieved with polymer flood. Many operational lessons have been learned and improvements have been made regarding polymer mixing, pumping, and onsite quality control, particularly in a challenging Arctic environment. Adequate polymer injection rate can be achieved with diligent monitoring and quality assurance of the properties of the injected polymer solution.

Ning, Samson↗
DOE OSTI2024

Progress of Gas Injection EOR Surveillance in the Bakken Unconventional Play—Technical Review and Machine Learning Study

Although considerable laboratory and modeling activities were performed to investigate the enhanced oil recovery (EOR) mechanisms and potential in unconventional reservoirs, only limited research has been reported to investigate actual EOR implementations and their surveillance in fields. Eleven EOR pilot tests that used CO2, rich gas, surfactant, water, etc., have been conducted in the Bakken unconventional play since 2008. Gas injection was involved in eight of these pilots with huff ‘n’ puff, flooding, and injectivity operations. Surveillance data, including daily production/injection rates, bottomhole injection pressure, gas composition, well logs, and tracer testing, were collected from these tests to generate time-series plots or analytics that can inform operators of downhole conditions. A technical review showed that pressure buildup, conformance issues, and timely gas breakthrough detection were some of the main challenges because of the interconnected fractures between injection and offset wells. The latest operation of co-injecting gas, water, and surfactant through the same injection well showed that these challenges could be mitigated by careful EOR design and continuous reservoir monitoring. Reservoir simulation and machine learning were then conducted for operators to rapidly predict EOR performance and take control actions to improve EOR outcomes in unconventional reservoirs.

Energy & Fuels↗
DOE OSTI2021

Autoencoder Based Analysis of RF Parameters in the Fermilab Low Energy Linac

Machine learning (ML) has the potential for significant impact on the modeling, operation, and control of particle accelerators due to its ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators. These algorithms work by identifying subtle behaviors of key variables prior to negative events. Efforts to apply ML to anomaly detection have largely focused on subsystems such as RF cavities, superconducting magnets, and losses in rings. However, dedicated efforts to understand how to apply ML for anomaly detection in linear accelerators have been limited. In this paper the use of autoencoders is explored to identify anomalous behavior in measured data from the Fermilab low-energy linear accelerator.

Edelen, Jonathan P. (ORCID:0000000215180652)↗