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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Squeezed Nanocrystals: Equilibrium Configuration of Metal Clusters Embedded Beneath the Surface of a Layered Material

Shapes of functional metallic nanocrystals, typically synthesized either free in solution or supported on surfaces, are key for controlling properties. Here, we consider a novel new class of metallic nanocrystals, copper clusters embedded near the surface of graphite, which can be considered a model system for metals embedded beneath surfaces of layered materials, or beneath supported membranes. We develop a continuum elasticity (CE) model for the equilibrium shape of these islands, and compare its predictions with experimental data. The CE model incorporates appropriate surface energy, adhesion energies, and strain energy. The agreement between the CE model and the data is—with one exception—excellent, both qualitatively and quantitatively, and is achieved with a single adjustable parameter. The model predicts that the embedded island shape is invariant with size, manifest both by constant side slope and by constant aspect ratio. This prediction is rationalized by dimensional analysis of the relevant energetic contributions. The aspect ratio of an embedded Cu cluster is much larger than that of a supported but non-embedded Cu cluster, due to resistance of the graphene membrane to deformation. Experimental data diverge from the model predictions only in the case of the aspect ratio of small islands, below a critical height of ~10 nm. The divergence may be due to bending strain, which is treated only approximately in the model. Strong support for the CE model and its interpretation is provided by additional data for embedded Fe clusters. Most of these observations and insights should be generally applicable to systems where a metal cluster is embedded beneath a layered material or supported membrane, provided that shape equilibration is possible.

Julien, Scott↗
DOE OSTI2021

Industrial control system device classification using network traffic features and neural network embeddings

Characterization of modern cyber–physical Industrial Control System (ICS) devices is critical to the evaluation of their security posture and an understanding of the underlying industrial processes with which they interact. In this work, we address two related ICS device identification tasks: (1) separating ICS from non-ICS devices and (2) identifying specific ICS device types. We propose two distinct methods (one based on the existing IP2Vec method, and a novel traffic-features-based method) for achieving the first task. For transferability of the first task between two datasets, the traffic-features-based method performs significantly better (75% overall accuracy) compared to IP2Vec (22.5% overall accuracy). We further propose a novel method called DNP2Vec to address the second task. DNP2Vec is evaluated on two different datasets and achieves perfect multi-class classification accuracy (100%) for both datasets.

42 ENGINEERING↗
DOE OSTI2017

Addressing embedded digital devices in safety-related systems of nuclear power plants - 307

The purpose of this paper is to provide an overview of the development of the approach to addressing embedded digital devices (EDD) in safety-related systems of nuclear power plants in the U.S. and around the world. In April, 2016, the U.S. Nuclear Regulatory Commission (US NRC) issued RIS 2016-05 to heighten awareness that EDDs might exist in procured equipment used in safety-related systems without the devices having been explicitly identified in procurement documentation. Inadequate consideration of these devices in digital technology system upgrades, component replacements, and new equipment applications could lead to an adverse safety consequence. The US NRC request was for addressees to implement early efforts to identify these devices. An area where this is being addressed is in regard to the performance of Diversity and Defense-In-Depth (D3) analyses, as addressed in US NRC Branch Technical Position 7-19. Nuclear facilities have increased their use and reliance on digital technology in systems and equipment (e.g., I and C, electrical systems, and fluid systems). In addition to digital instrumentation and control (I and C) systems, examples of safety-related equipment that may use digital technology include emergency diesel generators, pumps, valve actuators, motor control centers, breakers, priority logic modules, time-delay relays, and uninterruptible power sources. In the U.S. and around the world, engineering and licensing activities in standards and guidance have been, and are being developed to address this important consideration in protecting safety-related systems. This paper addresses development of an extended approach to evaluating common-cause failure vulnerability and mitigation in regard to equipment with an EDD. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A Mixed integer linear programming‐based distributed energy management for networked microgrids considering network operational objectives and constraints

Abstract Mixed integer linear programming (MILP)–based distributed energy management for networked microgrids embedded modern distribution systems is proposed. Considering the diverse ownership of microgrids, distributed energy resources (DERs) that interface directly with utilities and responsive loads, an alternating direction method of multipliers–based distributed framework was formulated for the scheduling of networked microgrids embedded modern distribution systems by adjusting nodal price signals iteratively. In addition, to make the formulated optimization problems resolvable through more accessible and popular MILP solvers, different linearisation techniques were employed to transform the nonlinear terms into linear or mixed integer linear formats. The proposed MILP‐based distributed method preserves all participants' autonomy (e.g., microgrids, DERs that interface directly with utilities and responsive loads), while incentivising them to actively participate in the distribution system operation with price signals. The proposed method is validated with results of numerical simulation using a modern distribution system consisting of multiple networked microgrids, DERs that interface directly with utilities, as well as responsive loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale

Abstract Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System , thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Sun, Shining↗
DOE OSTI2026

An efficient explicit implementation of a near-optimal quantum algorithm for simulating linear dissipative differential equations

We propose an efficient block-encoding technique for the implementation of the Linear Combination of Hamiltonian Simulations (LCHS) for simulating dissipative initial-value problems. This algorithm approximates a target nonunitary operator as a weighted sum of Hamiltonian evolutions, thereby emulating a dissipative problem by mixing various time scales. We introduce an efficient encoding of the LCHS into a quantum circuit based on a simple coordinate transformation that turns the dependence on the summation index into a trigonometric function. Classically, this method is equivalent to the use of a highly accurate Fejér-Clenshaw-Curtis quadrature formula. Quantumly, this significantly simplifies block-encoding of a dissipative problem and allows one to perform an exponential number of Hamiltonian simulations by a single Quantum Signal Processing (QSP) circuit. The resulting LCHS circuit has high success probability and the selector scales logarithmically with the number of terms in the LCHS sum and linearly with time. Careful analysis of error convergence proves that this method is more efficient than other LCHS circuits that have recently appeared in the literature. We verify the quantum circuit and its scaling by simulating it on a digital emulator of fault-tolerant quantum computers and, as a test problem, solve the advection-diffusion equation. The proposed algorithm can be used for simulating a wide class of nonunitary initial-value problems including the Liouville equation with added dissipation and linear embeddings of nonlinear systems, such as the Koopman-von Neumann and Carleman embeddings.

Novikau, I [Lawrence Livermore National Laboratory↗
DOE OSTI2017

Control and Cost-benefit Analysis of Fast Spreading Diseases: The case of Ebola

Mitigating the spread of infectious disease is of great importance for policy makers. Taking the recent outbreak of Ebola as an example, it was difficult for policy makers to identify the best course of action based on the cost-effectiveness of what was available. In effort to address the needs of policy makers to mitigate the spread of infectious disease before an outbreak becomes uncontrollable, we have devised a cost-benefit disease control model to simulate the effect of various control methods on disease incidence and the cost associated with each of the scenarios. Here, we present a case study of Ebola used to quantify the cost effectiveness of vaccination and isolation methods to minimize the spread of the disease. We evaluate the impact of changing strategy levels on the incidence of the disease and address the benefits of choosing one strategy over the other with regards to cost of vaccine and isolation. Disease. We use a general SEIRJ model for disease transmission. Here, S-Susceptible, E- Exposed (latent), IA– Infected (asymptomatic),IM– Infected (mild symptoms), IS– Infected (severe symptoms),JM– Isolated (mild symptoms at home), JS– Isolated (severe symptoms in hospital), and R- Recovered individuals. In this model, we consider the dynamics of the system and the effect of the relative transmissibility of isolated individuals (L) compared to other infected individuals. Cost. Ebola vaccination and treatment are very expensive and not widely available. Some preliminary data shows that it will take$73 million (M) to produce 27 M vaccines plus the cost for vaccine delivery and health care professionals (not included here). On the other hand, the treatment for Ebola in the U.S. would cost $25,000dollars a day per person to ensure proper isolation and adequate care(treatment, health care professionals, facilities and special equipment).Although not included in this research, the proper isolation of Ebola patients would also lead to a loss in hospital revenue of $148,000per day due to reduced patient capacity3. Here, we use $27,000 per individual hospitalized per day and $2.70 per person vaccinated. Model. To evaluate the cost-effectiveness of control methods on disease transmission, we assessed the affect of different levels of vaccination coverage on the resulting number of infected individuals. Then, we calculated the overall estimated cost of vaccination and resulting hospitalization for each scenario to identify the lowest cost-benefit ratio. Using a base population of 10 M individuals, we ran scenarios for different levels of vaccination (μ= 0.01, 0.05, 0.1) while varying the relative transmissibility of isolated individuals (L = 0.5, 0.6, 0.65).For each combination, we calculated the incidence, vaccination and hospitalization cost per individual per day (Fig 1). We note that an increase in the relative transmissibility of isolated individuals leads to a higher number of infected people and, therefore, a reduced number of candidates for vaccination and an overall increase in cost. Since the cost of vaccination is 1 ten-thousandth of the cost of hospitalization, our results clearly show the cost-benefit of vaccinating over hospital treatment. In every scenario studied, we observed a measurable reduction in disease incidence when vaccinating a higher fraction of the population compared to isolating individuals post infection. Given these preliminary results, we plan to extend the framework of our model to a dynamic control system where we consider the cost of vaccination and isolation embedded in the system of differential equations. This approach will allow us see the best available control implementation while minimizing the cost of treatment and vaccination.

EBOLA↗
DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Self-Replenishing Vascularized Fouling-Release Surfaces

Inspired by the long-term effectiveness of living antifouling materials, we have developed a method for the self-replenishment of synthetic biofouling-release surfaces. These surfaces are created by either molding or directly embedding 3D vascular systems into polydimethylsiloxane (PDMS) and filling them with a silicone oil to generate a nontoxic oil-infused material. When replenished with silicone oil from an outside source, these materials are capable of self-lubrication and continuous renewal of the interfacial fouling-release layer. Under accelerated lubricant loss conditions, fully infused vascularized samples retained significantly more lubricant than equivalent nonvascularized controls. Tests of lubricant-infused PDMS in static cultures of the infectious bacteria Staphylococcus aureus and Escherichia coli as well as the green microalgae Botryococcus braunii, Chlamydomonas reinhardtii, Dunaliella sauna, and Nannochloropsis oculata showed a significant reduction in biofilm adhesion compared to PDMS and glass controls containing no lubricant. Further experiments on vascularized versus nonvascularized samples that had been subjected to accelerated lubricant evaporation conditions for up to 48 h showed significantly less biofilm adherence on the vascularized surfaces. These results demonstrate the ability of an embedded lubricant-filled vascular network to improve the longevity of fouling-release surfaces.

Howell, C↗
DOE OSTI2017

Final Technical Report for Quantum Embedding for Correlated Electronic Structure in Large Systems and the Condensed Phase

This is the final technical report. We briefly describe some selected results below. Developments in density matrix embedding. DMET is a quantum embedding theory that we introduced at the beginning of the last funding period, around 2012-2013. Since the first DMET papers, which demonstrated proof-of- principle calculations on the Hubbard model and hydrogen rings, we have carried out a number of different developments, including: Extending the DMET technology to compute broken symmetry phases, including magnetic phases and super- conductivity (Pub. 13); Calibrating the accuracy of DMET and its cluster size convergence against other methods, and formulation of a dynamical cluster analog (Pubs. 4, 10) (see Fig. 1); Implementing DMET for ab-initio molecular calculations, and exploring different self-consistency criteria (Pubs. 9, 14); Using embedding to defi ne quantum classical interfaces Pub. 2; Formulating DMET for spectral functions (Pub. 7) (see Fig. 1); Extending DMET to coupled fermion-boson problems (Pub. 12). Together with these embedding developments, we have also implemented a wide variety of impurity solvers within our DMET framework, including DMRG (Pub. 3), AFQMC (Pub. 10), and coupled cluster theory (CC) (Pub. 9).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Internship Work Report

I worked on two projects during my summer internship at Sandia. My official title was “Intern - Mission Tech Electrical Eng./Computer Eng.- R&D Undergraduate Summer.” I worked at the central location, which is Albuquerque, New Mexico. The department you are placed in at Sandia doesn’t always correspond to the people you will be working with. For example, I only directly worked with one person from my department this summer. On one of my projects, I worked with a diverse team of engineers from many different departments. On my other project, I mainly worked with two departments, as the project had two distinct parts. As mentioned earlier, I worked on two projects during my summer at Sandia. The first project focused on a lightweight embedded controller in an advanced FPGA System-on-Chip for radar signal processing applications. The term “controller” refers to a hardware device that directs the flow of data between two entities. An FPGA is a reprogrammable integrated circuit (as opposed to an integrated circuit with one purpose). An FPGA was used on this project so in order to protype various ideas for our System-on-Chip. My role on the project was to implement designs on the fabric of the FPGA and design a state machine (written in C) for the processor. My second project was also heavily involved with embedded systems but had a different application. It focused on using a Newton-Raphson control algorithm to stabilize an inverted pendulum using a novel microcontroller. The pendulum dynamics were derived, and it was successfully simulated in MATLAB. I worked on integrating the microcontroller with the inverted pendulum machinery, and converting the Newton-Raphson control algorithm from MATLAB into C. The inverted pendulum was successfully stabilized using a simple PID controller and industry-standard microcontroller. The project is still ongoing, and the team is gearing up for more tests using the novel Newton-Raphson control algorithm and novel microcontroller

42 ENGINEERING↗
DOE OSTI2018

PERFORMANCE MODELING OF A NOVEL “SMART” MAGNETIC PARTICLE-EMBEDDED PCM LAYER FOR THERMAL MANAGEMENT SYSTEMS

Developing stable and environmentally friendly phase change materials (PCM) has been an active area of research due to many applications, such as industrial energy storage system, cooling/heating applications in buildings, and thermal management for electronics equipment as well as for batteries and photovoltaic modules. However, previous studies confirmed that most PCMs suffer a serious disadvantage of low thermal conductivity. A novel design of PCM layer is investigated here for thermal management application. The novel PCM layer is comprised of a PCM and magnetic particles coated on the shell. When a magnetic field is applied in the thermal management system, due to the magnetic of the particles, PCM layer are attached to the heat source to absorb heat, which significantly improves the heat transfer between PCM and the heat source. The melting temperature of the PCM and Curie temperature of the magnetic particles are carefully selected to optimize the performance and to ensure materials compatibility. A numerical study is conducted to reveal the heat transfer and performance improvement of the PCM layer comparing to the conventional PCMs.

Zhang, Mingkan↗
DOE OSTI2024

A Hermetic Package Technique for Multi-Functional Fiber Sensors through Pressure Boundary of Energy Systems Based on Glass Sealants

This paper presents a hermitic fiber sensor packaging technique that enables fiber sensors to be embedded in energy systems for performing multi-parameter measurements in high-temperature and strong radiation environments. A high-temperature stable Intrinsic Fabry–Perot interferometer (IFPI) array, inscribed by a femtosecond laser direct writing scheme, is used to measure both temperature and pressure induced strain changes. To address the large disparity in thermo-expansion coefficients (TECs) between silica fibers and metal parts, glass sealants with TEC between silica optical fibers and metals were used to hermetically seal optical fiber sensors inside stainless steel metal tubes. The hermetically sealed package is validated for helium leakages between 1 MPa and 10 MPa using a helium leak detector. An IFPI sensor embedded in glass sealant was used to measure pressure. The paper demonstrates an effective technique to deploy fiber sensors to perform multi-parameter measurements in a wide range of energy systems that utilize high temperatures and strong radiation environments to achieve efficient energy production.

Optics↗
DOE OSTI2015

Managing a Real-Time Embedded Linux Platform with Buildroot

Developers of real-time embedded software often need to build the operating system, kernel, tools and supporting applications from source to work with the differences in their hardware configuration. The first attempts to introduce Linux-based real-time embedded systems into the Fermilab accelerator controls system used this approach but it was found to be time-consuming, difficult to maintain and difficult to adapt to different hardware configurations. Buildroot is an open source build system with a menu-driven configuration tool (similar to the Linux kernel build system) that automates this process. A customized Buildroot [1] system has been developed for use in the Fermilab accelerator controls system that includes several hardware configuration profiles (including Intel, ARM and PowerPC) and packages for Fermilab support software. A bootable image file is produced containing the Linux kernel, shell and supporting software suite that varies from 3 to 20 megabytes large – ideal for network booting. The result is a platform that is easier to maintain and deploy in diverse hardware configurations

Diamond, J.↗
DOE OSTI2016

SU-G-JeP1-05: Clinical Impact of MLC Tracking for Lung SABR

Purpose: The objective of this study was to investigate the dosimetric benefits of multi-leaf collimator (MLC) tracking for lung SABR treatments in end-to-end clinically realistic planning and delivery scenarios. Methods: The clinical benefits of MLC tracking were assessed using previously delivered treatment plans and physical experiments. The 10 most recent single lesion lung SABR patients were re-planned following a 4D-GTV-based real-time adaptive protocol (PTV defined as the end-of-exhalation GTV plus 5.0 mm margins). The plans were delivered on a Trilogy Varian linac. Electromagnetic transponders (Calypso, Varian Medical Systems, USA) were embedded into a programmable moving phantom (HexaMotion platform) tracked with the Varian Calypso system. For each physical experiment, the MLC positions were collected and used as input for dose reconstruction. For both planned and physical experiments, the OAR dose metrics from the conventional and real-time adaptive SABR plans (Mean Lung Dose (MLD), V20 for lung, and near-maximum dose (D2%) for spine and heart) were statistically compared. The Wilcoxon test was used to compare plan and physical experiment dose metrics. Results: While maintaining target coverage, percentage reductions in dose metrics to the OARs were observed for both planned and physical experiments. Comparing the two plans showed MLD percentage reduction (MLDr) of 25.4% (absolute differences of 1.41 Gy) and 28.9% (1.29%) for the V20r. D2% percentage reduction for spine and heart were respectively 27.9% (0.3 Gy) and 20.2% (0.3 Gy). For the physical experiments, MLDr was 23.9% (1.3 Gy), and V20r 37.4% (1.6%). D2% reduction for spine and heart were respectively 27.3% (0.3 Gy) and 19.6% (0.3 Gy). For both plans and physical experiments, significant OAR dose differences (p<0.05) were found between the conventional SABR and real-time adaptive plans. Conclusion: Application of MLC tracking for lung SABR patients has the potential to reduce the dose to OARs during radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-FG-303-01: FEATURED PRESENTATION and BEST IN PHYSICS (THERAPY): Automating LINAC QA: Design and Testing of An Image Acquisition and Processing System Utilizing a Combination of Radioluminescent Phosphors, Embedded X-Ray Markers and Optical Measurements

Purpose: The recent development of phosphors to visualize radiation beams from linear accelerators (LINAC) offers a unique opportunity for evaluating radiation fields within the context of the treatment space. The purpose of this study was to establish an automated, self-calibrating prototype system for performing quality assurance (QA) measurements. Methods: A thin layer of Gd{sub 2}O{sub 2}S:Tb phosphor and fiducial markers were embedded on several planar faces of a custom-designed phantom. The phantom was arbitrarily placed near iso-center on the couch of a LINAC equipped with on-board megavoltage (MV) and kilovoltage (kV) imagers. A plan consisting of several beams and integrated image acquisitions was delivered. Images of the phantom were collected throughout the delivery. Salient features, such as fiducials, crosshairs and beam edges were then extracted from these images used to calibrate the system, adjust for variations in phantom placement, and perform measurements. Beam edges were visualized by imaging the light generated by the phosphor on the phantom enabling direct comparison with the light field and laser locations. Registration of MV, kV and optical image data was performed using the embedded fiducial markers, enabling comparison of imaging center locations. Measurements specified by TG-142 were calculated and compared with those obtained from a commercially available QA system. Results: The system was able to automatically extract the location of the fiducials, lasers, light field and radiation field from the acquired images regardless of phantom positioning. It was also able to automatically identify the locations of fiducial markers on kV and MV images. All collected measurements were within TG-142 guidelines. The difference between the prototype and commercially available system were less than 0.2 mm. Conclusion: The prototype system demonstrated the capability of accurately and autonomously evaluating various TG-142 parameters independent of operator input and phantom setup.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models

In a previous work, embedded ensemble propagation was proposed to improve the efficiency of sampling-based uncertainty quantification methods of computational models on emerging computational architectures. It consists of simultaneously evaluating the model for a subset of samples together, instead of evaluating them individually. A first approach introduced to solve parametric linear systems with ensemble propagation is ensemble reduction. In Krylov methods for example, this reduction consists in coupling the samples together using an inner product that sums the sample contributions. Ensemble reduction has the advantages of being able to use optimized implementations of BLAS functions and having a stopping criterion which involves only one scalar. However, the reduction potentially decreases the rate of convergence due to the gathering of the spectra of the samples. In this paper, we investigate a second approach: ensemble propagation without ensemble reduction in the case of GMRES. This second approach solves each sample simultaneously but independently to improve the convergence compared to ensemble reduction. This raises two new issues which are solved in this paper: the fact that optimized implementations of BLAS functions cannot be used anymore and that ensemble divergence, whereby individual samples within an ensemble must follow different code execution paths, can occur. We tackle those issues by implementing a high-performing ensemble GEMV and by using masks. The proposed ensemble GEMV leads to a similar cost per GMRES iteration for both approaches, i.e. with and without reduction. For illustration, we study the performances of the new linear solver in the context of a mesh tying problem. Furthermore, this example demonstrates improved ensemble propagation speed-up without reduction.

BLAS↗