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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Automatic learning rate adjustment for self-supervising autonomous robot control

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

Arras, Michael K.↗
DOE OSTI2020

A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience

Here, we study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in that it does not employ any form of “belief-averaging”. Instead, agents update their beliefs based on a min-rule. Under standard assumptions on the observation model and the network structure, we establish that each agent learns the truth asymptotically almost surely. As our main contribution, we prove that with probability 1, each false hypothesis is ruled out by every agent exponentially fast, at a network-independent rate that is strictly larger than existing rates. We then develop a computationally-efficient variant of our learning rule that is provably resilient to agents who do not behave as expected (as represented by a Byzantine adversary model) and deliberately try to spread misinformation.

42 ENGINEERING↗
DOE OSTI2025

Open Architecture for Cost Savings in Advanced Nuclear Reactors

Recently, nuclear power plant build projects in the West have run over budget due to high capital costs and schedule overruns. Compared to other sources of energy, nuclear power plants have higher capital costs. Reactors are often different at every site, resulting in a lack of standardization. Nuclear is expected to compete with other low carbon sources of energy which have lower capital costs making it essential for nuclear to develop ways of reducing costs. Strategies such as standardization, learning rates, modularization, and schedule reduction in advanced reactors can reduce nuclear costs by about 40%. Standardization as a way of cutting capital costs has been explored even in large nuclear power plants. Standardization of certain plant components can result in lower component and installation costs and higher learning from experience. Standardization can be achieved by adopting a criterion of key performance indicators and general design principles for a specific system or component such as the balance of plant. Modularization allows the construction of certain components of SMRs in a factory, which saves time, increases productivity, and encourages higher learning rates. Production learning decreases the time and the cost related to an activity. The potential for modularized components of advanced reactors to be manufactured in factories makes it conducive to achieving higher learning rates. Developing large-capacity nuclear programs through sequential builds cultivates a higher learning rate, which in effect may reduce schedule overruns. Open architecture has been identified as a way to drive standardization among advanced reactor designs and result in cost savings. Open architecture (OA) is defined as a design enabling a diverse supply chain by defining and publishing requirements of systems or equipment in functional and/or interface terms, utilizing technical standards in widespread use. Currently, the nuclear industry’s approach is to use closed architecture, making most designs proprietary. However, collaboration between various advanced reactor vendors and suppliers utilizing the concept of open architecture can result in modular and standardized architecture of subsystems or subcomponents of a nuclear power plant. Completely standardizing nuclear power plants may be impossible, however, certain common subsystems amongst the various reactor designs could be standardized and/or access a wider supply chain and leverage existing learning from other sectors. Open architecture will save time and allocate resources to the parts of the plants that have the most unique features. A key advantage of open architecture is its ability to improve production learning across advanced reactors (AR) types in the industry, by providing and utilizing the same kind of component. Sodium fast reactor (SFR), High Temperature Gas Reactor (HTGR) and Molten Salt Reactor (MSR) are the advanced reactors considered for this project. This paper aims to determine the cost savings in advanced reactor programs due to open architecture learning rate. This work is an extension of work done on light water reactor small modular reactors; the cost methodology was utilized to investigate the impact of open architecture on advanced reactors with a particular focus on sodium fast reactors. The cost data on sodium fast reactors used in the model presented the most adequate information required for the analysis.

Advanced Nuclear Reactors↗
NASA NTRS2005

Method of Real-Time Principal-Component Analysis

Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

Duong, Tuan↗
DOE OSTI2024

Meta-Analysis of Advanced Nuclear Reactor Cost Estimations

Supporting Data can be downloaded at: https://gain.inl.gov/content/uploads/4/2024/06/INL-RPT-24-77048-R1.xlsx Nuclear energy is a critical cornerstone of the current United States clean energy supply and may play a larger role in the future in support of a transition to a net-zero economy. The current fleet of nuclear reactors predominantly consists of large light-water reactors (LWRs), while many of the reactor designs under consideration are smaller and/or different technologies. Because these new designs have not yet been built, there is a high degree of uncertainty associated with their cost. This complicates energy-planning efforts because cost projections are not always standardized, consistent, and centralized in an easily accessible location. To help support energy planning in the US, this report provides advanced nuclear cost ranges using a transparent methodology along with other relevant information that can be used to help support decision making and energy planning. The purpose of this work was to conduct a methodical process for cost evaluation using only public information that was vetted with the end-goal to provide reference cost projections for nuclear energy. To provide a solid basis for these values, the approach and assumptions are explicitly laid out throughout the report allowing any user of the data to challenge or reconsider them. Because future US nuclear-reactor costs are still unknown due to little recent observed data, the report opted to compile a comprehensive list of bottom-up estimates and evaluate averages/trends within the data to identify reference ranges. This was deemed preferable to opining on the robustness or validity of one cost estimation versus another. To that end, the work evaluated thousands of lines of cost subaccounts from several bottom-up cost estimates. A wide variety of different reactor types captured in the data are of various sizes and technologies. Some of these reactors will be representative of advanced reactors under development while others will not. Thus, the results here are dependent on the data that are available and the accuracy of the estimates that are used. Each bottom-up estimate was reviewed to determine whether it was complete. Incomplete data sets were corrected to ensure an adequate basis of cross-comparison. The report is not without limitations and should be interpreted as an initial step to develop cost ranges for nuclear technology. Ultimately, future work can build upon the methodology with refined cost estimates to reduce uncertainty. US-based overnight capital cost (OCC) estimates were compiled from extensive data sets into ranges for both large and small reactor sizes for 2030. To project the cost declines over time, learning rates were sampled from literature sources. No SMRs were previously built; hence, learning rates based on bottom-up approaches (e.g., by quantifying the impact stemming from fabrication of different components, modular work, site construction, commissioning) were prioritized. For larger reactors, actual learning rates from deployments were used to project future costs (adjusted to account for standardization or lack thereof between designs). Other costs included are fixed and variable operations and maintenance costs. The final variables were capacity factors and ramp rates to support energy planning.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2005

Real-Time Principal-Component Analysis

A recently written computer program implements dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN), which was described in Method of Real-Time Principal-Component Analysis (NPO-40034) NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 59. To recapitulate: DOGEDYN is a method of sequential principal-component analysis (PCA) suitable for such applications as data compression and extraction of features from sets of data. In DOGEDYN, input data are represented as a sequence of vectors acquired at sampling times. The learning algorithm in DOGEDYN involves sequential extraction of principal vectors by means of a gradient descent in which only the dominant element is used at each iteration. Each iteration includes updating of elements of a weight matrix by amounts proportional to a dynamic initial learning rate chosen to increase the rate of convergence by compensating for the energy lost through the previous extraction of principal components. In comparison with a prior method of gradient-descent-based sequential PCA, DOGEDYN involves less computation and offers a greater rate of learning convergence. The sequential DOGEDYN computations require less memory than would parallel computations for the same purpose. The DOGEDYN software can be executed on a personal computer.

Duong, Vu↗
NASA NTRS1995

Locomotion training of legged robots using hybrid machine learning techniques

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results.

Simon, William E.↗
DOE OSTI2021

Machine learning based rate optimization under geologic uncertainty

We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. The training data for the model is generated by running the forward simulator with randomly assigned well rates using multiple geologic realizations. A reduced order representation of the permeability heterogeneity in each of the realizations is derived using a grid connectivity transformation (GCT). This step involves finding basis functions corresponding to the different modal frequencies of the grid connectivity represented by the grid Laplacian. The projection of the heterogeneous property field along these basis functions gives the basis coefficients that form the reduced order representation. Subsequently, for each training datapoint, streamlines are traced and the minimum time of flight (TOF) representing the tracer breakthrough time at each producer is recorded. The basis coefficients and well rates are fed to a machine learning model as input and the minimum TOF at the producers forms the output of the model. This trained model can then be used along with an optimizer for computing the optimal injection rates to maximize the injection sweep efficiency. This corresponds to minimizing the variance in the minimum TOF within each well group. Different architectures of neural network are tested using 5-fold cross validation to decide the best ML model to compute the streamline time of flight. The trained model is used to perform well rate optimization over multiple realizations of geology by using a risk tolerance penalty. The optimal well rates thus obtained are compared with two cases: a) equal well rates assigned to all injectors and producers and b) well rates obtained by optimizing over a single realization without considering the uncertainty in geology. The optimal well rates are seen to offer better oil recovery and sweep efficiency than both cases.

02 PETROLEUM↗
DOE OSTI2022

A Systematic Framework for Projecting the Future Cost of Offshore Wind Energy

Offshore wind costs are expected to decline rapidly in the short and medium term future as the industry grows and gains experience in manufacturing, installing, and operating commercial scale projects. Estimating the future costs of offshore wind energy is critical for evaluating the technology's economic performance, how it can fit into a broader clean energy economy, and how R&D investment can be allocated to advance the technology. We present a newly developed approach for forecasting these costs which focuses on an empirically-derived learning rate for capital costs and prescribed cost and performance improvements for operational costs and capacity factor. We establish baseline costs for a series of reference fixed-bottom and floating projects in 2021 and project cost trajectories to 2035, presenting both an average cost trajectory as well as describing the range of potential future costs associated with site-specific cost variations and uncertainty in the estimate of the learning rate. We also conduct sensitivity analyses showing the impact of different global deployments by 2035 and variations in the prescribed operational costs and capacity factors. The results show that fixed-bottom and floating offshore wind capital costs could decrease to around $\$$2,400/kW and $\$$3,300/kW by 2035, respectively, with ranges of $\$$2,100/kW - $\$$2,750/kW for fixed-bottom projects and $\$$2,850/kW - $\$$5,500/kW for floating projects. The levelized cost of energy of fixed-bottom and floating wind projects could decrease to $\$$53.1/MWh and $\$$63.9/MWh by 2035, with ranges of $\$$48.4/MWh - $\$$59.7/MWh for fixed-bottom projects and $\$$46.5/MWh - $\$$99.9/MWh for floating projects. By presenting the uncertainty associated with the forecast we provide a transparent description of the spectrum of potential cost trajectories for offshore wind.

17 WIND ENERGY↗
DOE OSTI2020

Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

Here we propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep and physics-informed neural networks. The local adaptation of activation function is achieved by introducing a scalable parameter in each layer (layer-wise) and for every neuron (neuron-wise) separately, and then optimizing it using a variant of stochastic gradient descent algorithm. In order to further increase the training speed, an activation slope-based slope recovery term is added in the loss function, which further accelerates convergence, thereby reducing the training cost. On the theoretical side, we prove that in the proposed method, the gradient descent algorithms are not attracted to sub-optimal critical points or local minima under practical conditions on the initialization and learning rate, and that the gradient dynamics of the proposed method is not achievable by base methods with any (adaptive) learning rates. We further show that the adaptive activation methods accelerate the convergence by implicitly multiplying conditioning matrices to the gradient of the base method without any explicit computation of the conditioning matrix and the matrix–vector product. The different adaptive activation functions are shown to induce different implicit conditioning matrices. Furthermore, the proposed methods with the slope recovery are shown to accelerate the training process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Active Learning with Irrelevant Examples

Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.

machine learning↗
DOE OSTI2022

BUTTER - Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

Array↗
DOE OSTI2026

Gravity Well Commercial Economics Assessment: Potential Revenue and Cost: Cooperative Research and Development (Final Report)

In the Gravity Well Revenue Study, we evaluate the potential revenue from energy storage using historical energy-only electricity prices, forward-looking projections of hourly electricity prices, and actual reported revenue. This analysis examines the impact of storage duration and round-trip efficiency, as well as the location of the storage, on storage revenue within the current and projected U.S. power system. We also investigated the impact of round-trip efficiency on storage revenue. We found that the relationship between storage revenue and round-trip efficiency is nonlinear. The value of improved round-trip efficiency declines as round-trip efficiency increases. In the Gravity Well Future Cost Study, we applied learning curves to predict the future cost trajectory of Gravity Wells (GrWs). Two types of analysis were implemented. The first was a bottom-up analysis that used historical learning rates for cost components, such as motors and gearboxes, and cost categories (e.g., engineering and design, etc.) to determine the learning-by-doing based single-factor learning curve. The single factor learning curve expresses the relationship between the cost of GrW and the number of units deployed (or the cumulative capacity). In the second analysis, we predicted future GrW costs via a top-down approach. This approach accounts for historical cost trends in other renewable energy and storage technologies, which have similarities with GrWs. Using a multifactor learning curve that accounts for both intrinsic (cumulative capacity) and extrinsic (the elasticity in the price of steel) factors, we estimated the future cost of GrWs.

25 ENERGY STORAGE↗
NASA NTRS2002

The Relationship Between Fidelity and Learning in Aviation Training and Assessment

Flight simulators can be designed to train pilots or assess their flight performance. Low-Fidelity simulators maximize the initial learning rate of novice pilots and minimize initial costs; whereas, expensive, high-fidelity simulators predict the realworld in-flight performance of expert pilots (Fink & Shriver, 1978 Hays & Singer 1989; Kinkade & Wheaton. 1972). Although intuitively appealing and intellectually convenient to generalize concepts of learning and assessment, what holds true for the role of fidelity in assessment may not always hold true for learning, and vice versa. To bring clarity to this issue, the author distinguishes the role of fidelity in learning from its role in assessment as a function of skill level by applying the hypothesis of Alessi (1988) and reviewing the Laughery, Ditzian, and Houtman (1982) study on simulator validity. Alessi hypothesized that there is it point beyond which one additional unit of flight-simulator fidelity results in a diminished rate of learning. The author of this current paper also suggests the existence of an optimal point beyond which one additional unit of flight-simulator fidelity results in a diminished rate of practical assessment of nonexpert pilot performance.

Noble, Cliff↗
NASA NTRS2005

Development of Advanced Verification and Validation Procedures and Tools for the Certification of Learning Systems in Aerospace Applications

Adaptive control technologies that incorporate learning algorithms have been proposed to enable automatic flight control and vehicle recovery, autonomous flight, and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments. In order for adaptive control systems to be used in safety-critical aerospace applications, they must be proven to be highly safe and reliable. Rigorous methods for adaptive software verification and validation must be developed to ensure that control system software failures will not occur. Of central importance in this regard is the need to establish reliable methods that guarantee convergent learning, rapid convergence (learning) rate, and algorithm stability. This paper presents the major problems of adaptive control systems that use learning to improve performance. The paper then presents the major procedures and tools presently developed or currently being developed to enable the verification, validation, and ultimate certification of these adaptive control systems. These technologies include the application of automated program analysis methods, techniques to improve the learning process, analytical methods to verify stability, methods to automatically synthesize code, simulation and test methods, and tools to provide on-line software assurance.

Jacklin, Stephen↗