Search NASA⌕ Search

SEARCH · Search NASA

Results for “test case”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Linear solvers for power grid optimization problems: A review of GPU-accelerated linear solvers

The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite, and they become extremely ill-conditioned as the interior method converges. These linear systems present a challenge for existing solver frameworks based on sparse LU or LDL T decompositions. Here, we benchmark five well known direct linear solver packages on CPU- and GPU-based hardware, using matrices extracted from power grid optimization problems. The achieved solution accuracy varies greatly among the packages. None of the tested packages delivers significant GPU acceleration for our test cases. For completeness of the comparison we include results for MA57, which is one of the most efficient and reliable CPU solvers for this class of problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A hybrid embedded discrete fracture model for simulating tight porous media with complex fracture systems

Numerical simulation considering complex fracture networks in tight oil reservoir becomes a hot issue during these years. Recently, embedded discrete fracture model (EDFM) shows great advantages over the DFM approaches in both calculation efficiency and simplicity of simulation workflow. EDFM has been widely used in tight oil/gas natural energy or enhanced oil recovery (EOR) development. If a large number of small-scale natural fractures exist in reservoir, the calculation efficiency of EDFM method may decrease. A new hybrid simulation model combining the EDFM and dual porosity (DP) model is proposed. The large-scale fractures are dealt with EDFM method while small scale fractures are dealt with DP model. Considering the low permeability of matrix, transient transfer between matrix and natural fracture is considered. The proposed hybrid model is validated compared with the local grid refinement (LGR) model and EDFM-MINC model, and the difference between transient transfer and pseudo-steady-state transfer effect on production is presented. Finally, two test cases are presented, and the sensitivity of some key parameters are investigated. Result shows that the new simulation model has higher calculation efficiency than LGR and EDFM-MINC models. Meanwhile, it honors a good accuracy. The pseudo-steady-state transfer between matrix and natural fracture leads to big error in tight oil production performance analysis both in single phase and two phases flow. Low matrix permeability and big matrix size increase the production difference between pseudo-steady-state transfer and transient transfer models. The proposed model can simulate reservoirs considering stochastic permeability distribution.

58 GEOSCIENCES↗
DOE OSTI2022

Frame invariant neural network closures for Kraichnan turbulence

Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial progress in developing parameterization (or closure) models for subgrid scale (SGS) processes using physical insights and mathematical approximations, they remain imperfect and can lead to inaccurate predictions. In recent years, machine learning has been successful in extracting complex patterns from high-resolution spatio-temporal data, leading to improved parameterization models, and ultimately better coarse grid prediction. However, the inability to satisfy known physics and poor generalization hinders the application of these models for real-world problems. In this work, we put forth a frame invariant closure approach to improve the accuracy and generalizability of deep learning-based subgrid scale closure models by embedding physical symmetries directly into the structure of the neural network. Specifically, we utilized specialized layers within the convolutional neural network in such a way that desired constraints are theoretically guaranteed without the need for any regularization terms. We demonstrate our framework for a two-dimensional decaying turbulence test case mostly characterized by the forward enstrophy cascade. We show that our frame invariant SGS model (i) accurately predicts the subgrid scale source term, (ii) respects the physical symmetries such as translation, Galilean, and rotation invariance, and (iii) is numerically stable when implemented in coarse-grid simulation with generalization to different initial conditions and Reynolds number. This work opens up a possibility of connecting physics-based theories and data-driven modeling paradigms, and thus represents a promising step towards the development of physically consistent data-driven turbulence closure models.

42 ENGINEERING↗
DOE OSTI2020

Time-series learning of latent-space dynamics for reduced-order model closure

In this work, we study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equations for an advection-dominated problem given by the viscous Burgers equation. Our formulation is devised in a nonintrusive manner with an equation-free evolution of dynamics in a reduced space with the latter being obtained through a proper orthogonal decomposition. In addition, we leverage the sequential nature of learning for both LSTMs and NODEs to demonstrate their capability for closure in systems that are not completely resolved in the reduced space. We assess our hypothesis for two advection-dominated problems given by the viscous Burgers equation. We observe that both LSTMs and NODEs are able to reproduce the effects of the absent scales for our test cases more effectively than does intrusive dynamics evolution through a Galerkin projection. This result empirically suggests that time-series learning techniques implicitly leverage a memory kernel for coarse-grained system closure as is suggested through the Mori–Zwanzig formalism.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers

In this article, we utilize machine learning to dynamically determine if a point on the computational grid requires implicit numerical dissipation for large eddy simulation (LES). The decision making process is learnt through a priori training on quantities derived from direct numerical simulation (DNS) data. In particular, we compute eddy-viscosities obtained through the coarse-graining of DNS quantities and utilize their projection onto a Gaussian distribution to categorize areas that may require dissipation. If our learning determines that closure is necessary, an upwinded scheme is utilized for computing the non-linear Jacobian. In contrast, if it is determined that closure is unnecessary, a symmetric and second-order accurate energy and enstrophy preserving Arakawa scheme is utilized instead. This results in a closure framework that precludes the specification of any model-form for the small scale contributions of turbulence but deploys an appropriate numerical dissipation from explicit closure driven hypotheses. Here, this methodology is deployed for the Kraichnan turbulence test-case and assessed through various statistical quantities such as angle-averaged kinetic energy spectra and vorticity structure functions. Our framework thus establishes a link between the use of explicit LES ideologies for closure and numerical dissipation-based modeling of turbulence leading to improved statistical fidelity of a posteriori simulations.

42 ENGINEERING↗
DOE OSTI2025

Influence of fertilization on the dynamics of energy use in wheat

Plant energy use is fundamental to plant survival and growth. However, we still lack effective means to quantify plant energy use strategies. This study introduced a concept quantifying the light level at which photochemical and non-photochemical energy use in plants are in equilibrium — the photochemical compensation point (PCCP) which can be determined with chlorophyll fluorescence measurements. We used winter wheat as a test case to explore the dynamics of PCCP and its physiological and biochemical regulations. Winter wheat PCCP decreased significantly across growth stages from jointing to grain filling. Long-term nitrogen and phosphate (NP) fertilization significantly increased PCCP, whereas potassium (K) and manure (M) fertilizer supplementation had negligible effects. PCCP exhibited significant positive correlations with leaf thickness, leaf P and sulfur (S), and stomatal conductance (gs) across all growth stages. All manure-amended treatments exhibited positive correlations of PCCP with leaf N, P, K and gs, and negative correlations with leaf calcium (Ca). Random forest analysis revealed that gs was the most significant predictor of PCCP variation, followed by leaf P, iWUE, and leaf thickness across all treatments. We suggest that plant energy use strategies are strongly coupled with plant water use strategies and nutrient availability through a complex interplay of effects on physiological and biochemical traits.

energy allocation↗
DOE OSTI2014

Theory and Implementation of Nuclear Safety System Codes - Part II: System Code Closure Relations, Validation, and Limitations

This is Part II of two articles describing the details of thermal-hydraulic sys- tem codes. In this second part of the article series, the system code closure relationships (used to model thermal and mechanical non-equilibrium and the coupling of the phases) for the governing equations are discussed and evaluated. These include several thermal and hydraulic models, such as heat transfer coefficients for various flow regimes, two phase pressure correlations, two phase friction correlations, drag coefficients and interfacial models be- tween the fields. These models are often developed from experimental data. The experiment conditions should be understood to evaluate the efficacy of the closure models. Code verification and validation, including Separate Effects Tests (SETs) and Integral effects tests (IETs) is also assessed. It can be shown from the assessments that the test cases cover a significant section of the system code capabilities, but some of the more advanced reactor designs will push the limits of validation for the codes. Lastly, the limitations of the codes are discussed by considering next generation power plants, such as Small Modular Reactors (SMRs), analyz- ing not only existing nuclear power plants, but also next generation nuclear power plants. The nuclear industry is developing new, innovative reactor designs, such as Small Modular Reactors (SMRs), High-Temperature Gas-cooled Reactors (HTGRs) and others. Sub-types of these reactor designs utilize pebbles, prismatic graphite moderators, helical steam generators, in- novative fuel types, and many other design features that may not be fully analyzed by current system codes. This second part completes the series on the comparison and evaluation of the selected reactor system codes by discussing the closure relations, val- idation and limitations. These two articles indicate areas where the models can be improved to adequately address issues with new reactor design and development.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Open-source MFIX-DEM software for gas-solids flows: Part II — Validation studies

With rapid advancements in computer hardware and numerical algorithms, computational fluid dynamics (CFD) has been increasingly employed as a useful tool for investigating the complex hydrodynamics inherent in multiphase flows. An important step during the development of a CFD model and prior to its application is conducting careful and comprehensive verification and validation studies. Accordingly, efforts to verify and validate the open-source MFIX-DEM software, which can be used for simulating the gas–solids flow using an Eulerian reference frame for the continuum fluid and a Lagrangian discrete framework (Discrete Element Method) for the particles, have been made at the National Energy Technology Laboratory (NETL). In part I of this paper, extensive verification studies were presented and in this part, detailed validation studies of MFIX-DEM are presented. A series of test cases covering a range of gas–solids flow applications were conducted. In particular the numerical results for the random packing of a binary particle mixture, the repose angle of a sandpile formed during a side charge process, velocity, granular temperature, and voidage profiles from a bounded granular shear flow, lateral voidage and velocity profiles from a monodisperse bubbling fluidized bed, lateral velocity profiles from a spouted bed, and the dynamics of segregation of a binary mixture in a bubbling bed were compared with available experimental data, and in some instances with empirical correlations. In addition, sensitivity studies were conducted for various parameters to quantify the error in the numerical simulation.

42 ENGINEERING↗
DOE OSTI2020

An error-controlled adaptive time-stepping method for particle advancement in coupled CFD-DEM simulations

Coupled Computational-Fluid-Dynamics (CFD) and Discrete-Element-Method (DEM) models provide an accurate description of multiphase physical systems where a solid granular particle phase exists in an underlying gaseous continuous medium. The time integration of the granular phase in these simulations is typically handled using an explicit scheme with a constant time-step among all particles that is invariant in time to resolve inter-particle collisions. A locally third-order accurate adaptive time integration technique for particles that employs an embedded locally second-order scheme for error determination is presented in this work. The particle time-step size is dynamically adapted based on solution error, thus leading to significant savings in computational time. The efficacy of our scheme is quantified using four test cases of varying complexity (binary collision, homogeneous cooling system, fluidized bed and hopper discharge). The adaptive time-stepping method exhibits improved performance (~ 2–3 times in most of the cases studied) compared to three commonly used non-adaptive time-step methods (first-order Euler-explicit, second-order Adams-Bashforth and third-order Runge-Kutta schemes), while maintaining the same level of accuracy and parallel scalability.

42 ENGINEERING↗
DOE OSTI2012

Hands-on Performance Tuning of 3D Finite Difference Earthquake Simulation on GPU Fermi Chipset

3D simulation of earthquake ground motion is one of the most challenging computational problems in science. The emergence of graphic processing units (GPU) as an effective alternative to traditional general purpose processors has become increasingly capable in terms of accelerating scientific computing research. In this paper, we describe our experiences in porting AWP-ODC, a 3D finite difference seismic wave propagation code, to the latest GPU Fermi chipset. We completely rewrote this Fortran-based 13-point asymmetric stencil computation code in C and MPI-CUDA in order to take advantage of the powerful GPU computing capabilities. Our new CUDA code implemented the asymmetric 3D stencil on Fermi to make the best use of GPU on-chip memory for an aggressive parallel efficiency. Benchmark on NVIDIA Tesla M2090 demonstrated 10x speedup versus the original fully optimized AWP-ODC FORTRAN MPI code running on a single Intel Nehalem 2.4 GHz CPU socket (4 cores/CPU), and 15x speedup versus the same MPI code running on a single AMD Istanbul 2.6 GHz CPU socket (6 cores/CPU). Sustained single-GPU performance of 143.8 GFLOPS in single precision is benchmarked for the testing case of 128x128x960 mesh size.

3D Stencil Computation↗
DOE OSTI2013

Using the PORS Problems to Examine Evolutionary Optimization of Multiscale Systems

Nearly all systems of practical interest are composed of parts assembled across multiple scales. For example, an agrodynamic system is composed of flora and fauna on one scale; soil types, slope, and water runoff on another scale; and management practice and yield on another scale. Or consider an advanced coal-fired power plant: combustion and pollutant formation occurs on one scale, the plant components on another scale, and the overall performance of the power system is measured on another. In spite of this, there are few practical tools for the optimization of multiscale systems. This paper examines multiscale optimization of systems composed of discrete elements using the plus-one-recall-store (PORS) problem as a test case or study problem for multiscale systems. From this study, it is found that by recognizing the constraints and patterns present in discrete multiscale systems, the solution time can be significantly reduced and much more complex problems can be optimized.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Spectral solver for multi-scale plasma physics simulations with dynamically adaptive number of moments

A spectral method for kinetic plasma simulations based on the expansion of the velocity distribution function in a variable number of Hermite polynomials is presented. The method is based on a set of non-linear equations that is solved to determine the coefficients of the Hermite expansion satisfying the Vlasov and Poisson equations. In this paper, we first show that this technique combines the fluid and kinetic approaches into one framework. Second, we present an adaptive strategy to increase and decrease the number of Hermite functions dynamically during the simulation. The technique is applied to the Landau damping and two-stream instability test problems. Performance results show 21% and 47% saving of total simulation time in the Landau and two-stream instability test cases, respectively.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

A mathematical programming approach for the optimal placement of flame detectors in petrochemical facilities

Flame detectors provide an important layer of protection for personnel in petrochemical plants, but effective placement can be challenging. A mixed-integer nonlinear programming formulation is proposed for optimal placement of flame detectors while considering non-uniform probabilities of detection failure. We show that this approach allows for the placement of fire detectors using a fixed sensor budget and outperforms models that do not account for imperfect detection. In this study, we develop a linear relaxation to the formulation and an efficient solution algorithm that achieves global optimality with reasonable computational effort. We integrate this problem formulation into the Python package, Chama, and demonstrate the effectiveness of this formulation on a small test case and on two real-world case studies using the fire and gas mapping software, Kenexis Effigy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Impacts of the morphology of new neighborhoods on microclimate and building energy

In anticipation of emerging global urbanization and consequent increases in energy use and carbon dioxide emissions, better understanding and quantification of climate effects on energy use in cities are needed, requiring coordinated research into large-scale, regional, and microclimate impacts to and from the city structure. The methodology described here addresses this need by (1) demonstrating a process for creating and testing example morphologies for new neighborhoods for their impact on local and regional meteorology within a two-way-coupled four-domain nested mesoscale weather model (6 km horizontal resolution outer domain, 90 m horizontal innermost domain) and (2) allocating resulting building-level meteorological profiles to each building in a neighborhood for parallel computation of building-by-building energy use. Our Chicago Loop test case shows that the morphology of even a small new added development to a neighborhood affects not only its own microclimate, but also the microclimate of the original neighborhood to which the development was added, and that these changes in microclimate affect both neighborhoods’ building energy use. Furthermore, this method represents an important step toward quantifying and analyzing the relationships among climatic conditions, urban morphology, and energy use and using these relationships to inform energy-efficient urban development and planning.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches

Regulations corroborate the importance of retrofitting existing building stocks or constructing new energy-efficient districts. There is, thus, a need for modeling tools to evaluate energy scenarios to better manage and design cities, and numerous methodologies and tools have been developed. Among them, Urban Building Energy Modelling (UBEM) tools allow the energy simulation of buildings at large scales. Choosing an appropriate UBEM tool, balancing the level of complexity, accuracy, usability, and computing needs, remains a challenge for users. The review focuses on the main bottom-up physics-based UBEM tools, comparing them from a user-oriented perspective. Five categories are used: (i) the required inputs, (ii) the reported outputs, (iii) the exploited workflow, (iv) the applicability of each tool, and (v) the potential users. Moreover, a critical discussion is proposed, focusing on interests and trends in research and development. The results highlighted major differences between UBEM tools that must be considered to choose the proper one for an application. Finally, barriers of adoption of UBEM tools include the needs of a standardized ontology, a common three-dimensional city model, a standard procedure to collect data, and a standard set of test cases. This feeds into future development of UBEM tools to support cities’ sustainability goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Renewable energy analysis in indigenous communities using bottom-up demand prediction

This paper provides a methodology for the holistic analysis of hybrid renewable energy systems in rural communities. Electric demand is an important component for modeling and analysis of renewable energy systems. Typically, electric demand data is not available due to the internal privacy policies of utility providers. Therefore, this study proposes the use of bottom-up approaches for the development of the electric demand profile, considering the general homogeneity of residential and commercial buildings in rural communities. As a test case, this study develops the electric demand profile and investigates the technical and environmental feasibility of a hybrid renewable energy system for the New Town community on the Fort Berthold Indian Reservation (FBIR) in North Dakota. This study conducts the hybrid renewable energy system’s analysis by developing scripts in the LK scripting language and integrating System Advisor Model software’s open-source modules for modeling of renewable energy systems. Here, the results for the validation testbed of this study show that hybrid renewable resources have higher ratios of energy used for self-consumption to the total energy generated compared to stand-alone wind and PV farms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

New adaptive method to optimize the secondary reflector of linear Fresnel collectors

Performance of linear Fresnel collectors may largely depend on the secondary-reflector profile design when small-aperture absorbers are used. Optimization of the secondary-reflector profile is an extremely challenging task because there is no established theory to ensure superior performance of derived profiles. In this work, an innovative optimization method is proposed to optimize the secondary-reflector profile of a generic linear Fresnel configuration. The method correctly and accurately captures impacts of both geometric and optical aspects of a linear Fresnel collector to secondary-reflector design. The proposed method is an adaptive approach that does not assume a secondary shape of any particular form, but rather, starts at a single edge point and adaptively constructs the next surface point to maximize the reflected power to be reflected to absorber(s). As a test case, the proposed optimization method is applied to an industrial linear Fresnel configuration, and the results show that the derived optimal secondary reflector is able to redirect more than 90% of the power to the absorber in a wide range of incidence angles. Here, the proposed method can be naturally extended to other types of solar collectors as well, and it will be a valuable tool for solar-collector designs with a secondary reflector.

14 SOLAR ENERGY↗