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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

P- and L-Band Retrieval of Subsurface Soil Moisture and Temperature Profiles as First-Order Polynomial Function

This paper demonstrates the potential use of P and L band passive measurements to determine root zone soil moisture (SM) and soil temperature(ST). SM and ST data have been taken as a function of depth during the NASA GSFC PLEX19 experiment in the summer of 2019 at Beltsville, MD, USA. Using these data, a coherent model has been used to compute H and V brightness temperatures at frequencies of 0.8 and 1.4 GHz with an observation angle of 35 degrees. These synthetic brightness data are then used to estimate the SM and ST profiles which are represented by linear polynomials. The inversion problem is formulated as a least square problem that is solved by a global optimization method known as the Adaptive Simulated Annealing(ASA) method. Four inversion examples having different SM and ST profiles are presented. Selected results show that the standard deviation between the retrieved and measured data is less than 0.077 cm3/cm3 for SM, and 2.245 °C for ST.

Ming Li↗
NASA NTRSDate not supplied

A New ML-Based Adaptive Thinning Methodology to Improve the Impact of AIRS and CrIS Assimilation on Global Tropical Cyclone Forecasts

This work builds on previous research performed by this team to improve the forecast of Tropical Cyclones (TCs) by assimilating AIRS and CrIS radiances into the NASA Global Earth Observing System (GEOS). Past published work demonstrated that the assimilation of radiances with variable density was beneficial to TC forecasting in the GEOS. In the previous setup, a fixed-size moving square named 'TC domain' was activated by the so-called TC-vitals, an international real-time message accessible to all NWP forecasting centers, that documents the existence of a TC, its estimated position, and its size. The information from TC-vitals activated a switch in the GEOS, which allowed to reduce the distance used for thinning AIRS and CrIS data inside a 15 degrees by 15 degrees moving TC domain centered on the storm, so that more data were assimilated in the vicinity of the TC during its lifetime. The methodology produced improved TC analyses and led to better forecasts, particularly related to intensity, without damaging the global forecast skill. In the new version, the adaptive thinning methodology is based on a machine-learning technique. The technique searches for TCs and creates TC masks by using cloud-top temperatures from all geostationary satellites without the need for additional information. It is being trained against the International Best Track Archive for Climate Stewardship (IBTrACS) data base. Once a TC mask is created, a switch identical to the one used in the previous adaptive thinning method is activated, allowing the GEOS to ingest more data in the TC-shaped size-changing domain that follows the storm. As of today, the team has been able to successfully assimilate data inside the ML-detected TC domains. Future work includes an improved capability of reducing false alarm rates (i.e., cloud systems that are erroneously labeled as TCs).

Oreste Reale↗
DOE OSTI2021

Tailored computational approaches to interrogate heavy element chemistry and structure in condensed phase

In this chapter we are presenting a brief review of the challenges encountered in the study of 4f and 5f block elements in the condensed phase. Their recovery, use in molten salt reactors and other interesting applications necessitate the use of molecular dynamics and large-scale models that take into account both the electronic structure and relativistic corrections. Sampling the multitude of electronic and atomic configurational states is at the heart of reliable predictions of structure, reactivity, dynamics and transport of heavy metals in complex environments. We present three examples that combine lanthanide elements with large scale models: i) our recent developments of a versatile adaptive learning method that enables global optimization in high dimensional spaces, ii) results of computed pKa values of lanthanide aqua complexes, and iii) structure and computed EXAFS of heavy elements in molten salts. The latter two employ our recently-optimized lanthanide pseudo-potentials and companion basis sets for condensed phase ab initio molecular dynamics.

Nguyen, Manh Thuong↗
DOE OSTI2020

Port Hope Area Initiative - Port Granby Project: Managing Highly Heterogeneous Low-Level Radioactive Waste - 20327

The Port Granby Project is a large remediation project currently approximately 95% complete. Initial waste characterization was provided through a borehole sampling program. As the waste was excavated, it was found to be highly heterogeneous, ranging from essentially 'clean' material to hundreds of Bq/g. This heterogeneity resulted in constantly changing site conditions. Methods to adapt to these conditions are described. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Airships as Transformative Tools for Power Systems Logistics: Addressing Infrastructure Challenges and Enhancing Disaster Resilience

Airships represent a transformative advancement in logistics, particularly for the power systems industry. Their unique ability to transport heavy equipment and personnel efficiently over remote or disaster-stricken areas, while requiring minimal infrastructure, addresses critical gaps left by traditional methods. Combining adaptability with environmental sustainability, airships offer a low-carbon alternative for routine operations and emergency responses. This review presents current developments in airship technology, such as rigid airships designed for large payloads, demonstrate their potential to revolutionize supply chains.

Fuhr, Peter L. [ORNL] (ORCID:0000000211627445)↗
NASA NTRS2011

Hybrid Adaptive Flight Control with Model Inversion Adaptation

This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

Nguyen, Nhan↗
NASA NTRS1991

The Doubly Labeled Water Method for Measuring Human Energy Expenditure: Adaptations for Spaceflight

It is essential to determine human energy requirements in space, and the doubly labeled water method has been identified as the most appropriate means of indirect calorimetry to meet this need. The method employs naturally occurring, stable isotopes of hydrogen (H-2, deuterium) and oxygen (O-18) which, after dosing, mix with body water. The deuterium is lost from the body as water while the O-18 is eliminated as both water and CO2. The difference between the two isotope elimination rates is therefore a measure of CO2 production and hence energy expenditure. Spaceflight will present a unique challenge to the application of the doubly labeled water method. Specifically, interpretation of doubly labeled water results assumes that the natural abundance or 'background' levels of the isotopes remain constant during the measurement interval. To address this issue, an equilibration model will be developed in an ongoing ground-based study. As energy requirements of women matched to counterparts in the Astronauts Corps are being determined by doubly labeled water, the baseline isotope concentration will be changed by consumption of 'simulated Shuttle water' which is artificially enriched. One group of subjects will be equilibrated on simulated Shuttle water prior to energy determinations by doubly labeled water while the others will consume simulated Shuttle water after dosing. This process will allow us to derive a prediction equation to mathematically model the effect of changing background isotope concentrations.

Schulz, Leslie O.↗
NASA NTRS1994

Development of an adaptive hp-version finite element method for computational optimal control

In this research effort, the usefulness of hp-version finite elements and adaptive solution-refinement techniques in generating numerical solutions to optimal control problems has been investigated. Under NAG-939, a general FORTRAN code was developed which approximated solutions to optimal control problems with control constraints and state constraints. Within that methodology, to get high-order accuracy in solutions, the finite element mesh would have to be refined repeatedly through bisection of the entire mesh in a given phase. In the current research effort, the order of the shape functions in each element has been made a variable, giving more flexibility in error reduction and smoothing. Similarly, individual elements can each be subdivided into many pieces, depending on the local error indicator, while other parts of the mesh remain coarsely discretized. The problem remains to reduce and smooth the error while still keeping computational effort reasonable enough to calculate time histories in a short enough time for on-board applications.

Hodges, Dewey H.↗
DOE OSTI2022

A particle resolved simulation approach for studying shock interactions with moving, colliding solid particles

This work applies a new combination of techniques for the fully resolved simulation of compressible, gas–particle multiphase flows. Here, the adaptive wavelet collocation method is used to dynamically, and efficiently, adapt the computational grid to localized flow features and the particles. A characteristic-based volume penalization method that imposes arbitrary Dirichlet, Neumann, or Robin-type immersed boundary conditions, is used to enforce the no-slip condition at particle surfaces. A hard-sphere collision model is applied to capture the particle–particle collisions. Proof of concept test cases are presented, showcasing the dynamic grid adaptation and fully resolved two-way coupling between the phases that is possible with this approach. Results for a shock-driven single cylinder under viscous and inviscid conditions are presented along with a demonstration of a shock interacting with a cloud of randomly distributed cylinders and spheres.

42 ENGINEERING↗
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 OSTI2025

Advancing simulations of coupled electron and phonon nonequilibrium dynamics using adaptive and multirate time integration

Electronic structure calculations in the time domain provide a deeper understanding of nonequilibrium dynamics in materials. The real-time Boltzmann equation (rt-BTE), used in conjunction with accurate interactions computed from first principles, has enabled reliable predictions of coupled electron and lattice dynamics. However, the timescales and system sizes accessible with this approach are still limited, with two main challenges being the different timescales of electron and phonon interactions and the cost of computing collision integrals. As a result, only a few examples of these calculations exist, mainly for two-dimensional (2D) materials. Here we leverage adaptive and multirate time integration methods to achieve a major step forward in solving the coupled rt-BTEs for electrons and phonons. Relative to conventional (non-adaptive) time-stepping, our approach achieves a 10x speedup for a target accuracy, or greater accuracy by 3–6 orders of magnitude for the same computational cost, enabling efficient calculations in both 2D and bulk materials. This efficiency is showcased by computing the coupled electron and lattice dynamics in graphene up to ~100 ps, as well as modeling ultrafast lattice dynamics and thermal diffuse scattering maps in bulk materials (silicon and gallium arsenide). In addition to improved efficiency, our adaptive method can resolve the characteristic rates of different physical processes, thus naturally bridging different timescales. This enables simulations of longer timescales and provides a framework for modeling multiscale dynamics of coupled degrees of freedom in matter. Our work opens new opportunities for quantitative studies of nonequilibrium physics in materials, including driven lattice dynamics with phonons coupled to electrons, spin, and other degrees of freedom.

Yao, Jia [California Institute of Technology (CalT↗
NASA NTRS2009

Adjustment of Adaptive Gain with Bounded Linear Stability Analysis to Improve Time-Delay Margin for Metrics-Driven Adaptive Control

This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a linear damaged twin-engine generic transport model of aircraft. The analysis shows that the system with the adjusted adaptive gain becomes more robust to unmodeled dynamics or time delay.

Bakhtiari-Nejad, Maryam↗
DOE OSTI2022

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss function of the neural network, and have been successfully employed to solve diverse forward and inverse PDE problems. However, one disadvantage of the first generation of PINNs is that they usually have limited accuracy even with many training points. Here, we propose a new method, gradient-enhanced physics-informed neural networks (gPINNs), for improving the accuracy of PINNs. gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function. We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems. Our numerical results show that gPINN performs better than PINN with fewer training points. Additionally, we combined gPINN with the method of residual-based adaptive refinement (RAR), a method for improving the distribution of training points adaptively during training, to further improve the performance of gPINN, especially in PDEs with solutions that have steep gradients.

42 ENGINEERING↗
NASA NTRS2016

Uncertainty Estimates of Psychoacoustic Thresholds Obtained from Group Tests

Adaptive psychoacoustic test methods, in which the next signal level depends on the response to the previous signal, are the most efficient for determining psychoacoustic thresholds of individual subjects. In many tests conducted in the NASA psychoacoustic labs, the goal is to determine thresholds representative of the general population. To do this economically, non-adaptive testing methods are used in which three or four subjects are tested at the same time with predetermined signal levels. This approach requires us to identify techniques for assessing the uncertainty in resulting group-average psychoacoustic thresholds. In this presentation we examine the Delta Method of frequentist statistics, the Generalized Linear Model (GLM), the Nonparametric Bootstrap, a frequentist method, and Markov Chain Monte Carlo Posterior Estimation and a Bayesian approach. Each technique is exercised on a manufactured, theoretical dataset and then on datasets from two psychoacoustics facilities at NASA. The Delta Method is the simplest to implement and accurate for the cases studied. The GLM is found to be the least robust, and the Bootstrap takes the longest to calculate. The Bayesian Posterior Estimate is the most versatile technique examined because it allows the inclusion of prior information.

Rathsam, Jonathan↗