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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

ZettaBricks: A Language Compiler and Runtime System for Anyscale Computing

This grant supported the ZettaBricks and OpenTuner projects. ZettaBricks is a new implicitly parallel language and compiler where defining multiple implementations of multiple algorithms to solve a problem is the natural way of programming. ZettaBricks makes algorithmic choice a first class construct of the language. Choices are provided in a way that also allows our compiler to tune at a finer granularity. The ZettaBricks compiler autotunes programs by making both fine-grained as well as algorithmic choices. Choices also include different automatic parallelization techniques, data distributions, algorithmic parameters, transformations, and blocking. Additionally, ZettaBricks introduces novel techniques to autotune algorithms for different convergence criteria. When choosing between various direct and iterative methods, the ZettaBricks compiler is able to tune a program in such a way that delivers near-optimal efficiency for any desired level of accuracy. The compiler has the flexibility of utilizing different convergence criteria for the various components within a single algorithm, providing the user with accuracy choice alongside algorithmic choice. OpenTuner is a generalization of the experience gained in building an autotuner for ZettaBricks. OpenTuner is a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

2016 High Performance Computing Annual Report

Sandia National Laboratories’ California site is celebrating its 60th anniversary (1956 to 2016), and high performance computing has been a key enabler for its scientists and engineers throughout much of its history. Since its founding, Sandia California has helped pioneer the use of HPC platforms including hosting Sandia’s first Cray-1 supercomputer in the 1970s and supporting development of scalable cluster computing platforms to create a new paradigm for cost-effective supercomputing in the 1990s. Recent decades of investment in creation of scalable application frameworks for scientific computing have also enabled new generations of modeling and simulation codes. These resources have facilitated computational analysis of complex phenomena in diverse applications spanning national defense, energy, and homeland security. Today, Sandia California researchers work with partners in academia, industry, and national labs to evolve the state-of-the-art in HPC, modeling, and data analysis (including foundational capabilities for exascale computing platforms) and apply them in transformational ways. Research efforts include mitigating the effects of silent hardware failures that can jeopardize the results of large-scale computations, developing exascale-capable asynchronous task-parallel programming models and runtime systems, formulating new techniques to better explore and analyze extreme-scale data sets, and increasing our understanding of materials and chemical sciences which has applications spanning nuclear weapons stockpile stewardship to more efficient automobile engines. The following section highlights some of these research and applications projects and further illustrates the breadth of our HPC capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Architecture and Implementation of OpenPET Firmware and Embedded Software

OpenPET is an open source, modular, extendible, and high-performance platform suitable for multi-channel data acquisition and analysis. Due to the versatility of the hardware, firmware, and software architectures, the platform is capable of interfacing with a wide variety of detector modules not only in medical imaging but also in homeland security applications. Analog signals from radiation detectors share similar characteristics-a pulse whose area is proportional to the deposited energy and whose leading edge is used to extract a timing signal. As a result, a generic design method of the platform is adopted for the hardware, firmware, and software architectures and implementations. The analog front-end is hosted on a module called a Detector Board, where each board can filter, combine, timestamp, and process multiple channels independently. The processed data is formatted and sent through a backplane bus to a module called Support Board, where 1 Support Board can host up to eight Detector Board modules. The data in the Support Board, coming from 8 Detector Board modules, can be aggregated or correlated (if needed) depending on the algorithm implemented or runtime mode selected. It is then sent out to a computer workstation for further processing. The number of channels (detector modules), to be processed, mandates the overall OpenPET System Configuration, which is designed to handle up to 1,024 channels using 16-channel Detector Boards in the Standard System Configuration and 16,384 channels using 32-channel Detector Boards in the Large System Configuration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms

Abstract Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Ramôa, Mafalda (ORCID:0000000302187801)↗
DOE OSTI2025

Zigzag flow reactor for weekly thermochemical energy storage

This paper describes theoretical models and experimental performance of a novel Zigzag Flow Reactor (ZFR) for weekly thermochemical energy storage. The ZFR reduces redox-active metal oxide (MO x ) particles at high temperature (up to ~1100 °C) under inert gas sweep. A physical model demonstrates the approach to process equilibrium by minimizing the associated exergy destruction in a finite number of reaction steps, establishing the thermodynamic requirements for a practical reactor. The model results show several cost-relevant parameter tradeoffs, and the tradeoff analysis implies a cost-optimized set of boundary conditions. Numerical models and prototypes show that the ZFR enables significant gas phase homogenization while simultaneously enabling a customizable MO x residence time in the reactor, both key requirements for approaching an equilibrium process. A scaling model demonstrates the simplicity and affordability of sizing the ZFR to grid-scale levels, with fabrication costs at least five times lower than previously proposed scalable reactor concepts. As a result, a laboratory ZFR prototype achieved an energy storage density of ~90 Wh/kg with CaAl 0.2 Mn 0.8 O 3-δ as the MO x , at temperatures of ~850 °C in >10 h of total runtime.

Thermochemical energy storage↗
DOE OSTI2020

A Holistic Algorithmic Approach to Improving Accuracy, Robustness, and Computational Efficiency for Atmospheric Dynamics

Atmospheric weather and climate models must perform simulations very quickly to be useful. Therefore, modelers have traditionally focused on reducing computations as much as possible. However, in our new era of increasingly compute-capable hardware, data movement is now the prohibiting expense. This study examines the computational benefits of a new algorithmic approach to modeling atmospheric dynamics on scales relevant to weather and climate simulation. Rather than minimizing computations, this new approach considers the larger problem more holistically, including spatial accuracy, temporal accuracy, robustness (i.e., oscillations), on-node efficiency, and internode data transfers together at once. Numerical experiments demonstrate how computations can be strategically increased to simultaneously address each of these constraints while reducing data movement to adapt to modern accelerated hardware. The new algorithm can achieve at times up to 80% peak floating point throughput in single precision on the Nvidia Tesla V100 GPU, where the traditional approach is shown to only achieve single-digit floating point efficiency. Further, the new algorithm is twice as fast as a standard Runge--Kutta time integrator, and high-order accuracy with Weighted Essentially Non-Oscillatory (WENO) limiting came at less than 30% additional runtime cost on a GPU, thus increasing the accuracy per degree of freedom.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Drone energy consumption↗
NASA NTRSDate not supplied

Dynamic Assurance of Autonomous Systems Through Ground Control Software

Assurance cases have emerged as a way to build trust in complex autonomous systems. Many assurance case justifications for such systems need to be constantly reevaluated based on the current system context and performance. Autonomous systems, especially those deployed in remote environments, often have a ground control system that enables monitoring and remote operations. In this paper, we propose a dynamic assurance framework that aims at connecting the assurance case with the ground control system. We use the ground control system to facilitate dynamic evaluation of quantitative assurance measures that support various justifications in the assurance case. We demonstrate the proposed dynamic assurance framework on the NASA Ames Research Center project Troupe. We use a combination of in-house and external tools to identify the assurance measures, formalize the related requirements, and generate monitors that feed the data to the external ground control system.

dynamic assurance case↗
NASA NTRS2018

DejaVu: A Monitoring Tool for First-Order Temporal Logic

Runtime Verification (rv) is aimed at analyzing individual execution traces and temporal behaviors observed from running programs and systems. Its traditional purpose is in detecting the lack of conformance with respect to a formal specification. While very early rv systems were based on specifications given in some form of propositional temporal logic, recent efforts have focused on monitoring so-called parametric specifications over events that carry data. Since a monitor for such specifications has to store observed data, the challenge is to have an efficient representation and manipulation of data. The fundamental problem is that the actual values of the data are not necessarily bounded or provided in advance. In this paper, we describe our monitoring tool, DejaVu, which implements our algorithm [HPU17] for monitoring first-order past linear-time temporal logic over a sequence of events that carry data. We propose the use of Binary Decision Diagrams (bdds) [Bry86] for representing and manipulating sets of observed data since (1) bdds provide highly compact representations, (2) operations over bdds, in particular complementation, are very efficient, and (3) the monitor construction for the propositional case shown in [HR02] naturally extends to bdds. Our experiments show a substantial improvement in performance compared to a related tool.

Ulus, Dogan↗
DOE OSTI2022

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

The “curse of dimensionality” has limited the applicability and expansion of tabulated combustion models. While the tabulated flamelet model and other multi-dimensional manifold approaches have shown predictive capability, the associated tabulation involves the storage of large lookup tables, requiring large memory as well as multi-dimensional interpolation subroutines, all implemented during runtime. As such, this work investigates the use of deep artificial neural networks to replace lookup tables in order to reduce the memory footprint and increase the computational speed of tabulated flamelets and related approaches. Specifically, different strategic approaches to training the artificial neural network models are explored and a grouped multi-target artificial neural network is introduced, which takes advantage of the ability of artificial neural networks to map an input space to multiple targets by classifying the species based on their correlation to one another. The grouped multi-target artificial neural network approach is validated by applying it to an n-dodecane spray flame using conditions of the Spray A flame from the Engine Combustion Network and comparing global flame characteristics for different ambient conditions using a well-established large-eddy simulation framework. The same framework is then extended to the simulations of methyl decanoate combustion in a compression ignition engine. The validation studies show that the grouped multi-target artificial neural networks are able to accurately capture flame liftoff, autoignition, two-stage heat release and other quantitative trends over a range of conditions. The use of neural networks in conjunction with the grouping mechanism as performed in the grouped multi-target artificial neural network produces a significant reduction in the memory footprint and computational costs for the code and, thus, widens the operating envelope for higher fidelity engine simulations with detailed mechanisms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

IRIS: A Performance-Portable Framework for Cross-Platform Heterogeneous Computing

From edge to exascale, computer architectures are becoming more heterogeneous and complex. The systems typically have fat nodes, with multicore CPUs and multiple hardware accelerators such as GPUs, FPGAs, and DSPs. This complexity is causing a crisis in programming systems and performance portability. Several programming systems are working to address these challenges, but the increasing architectural diversity is forcing software stacks and applications to be specialized for each architecture. As we show, all of these approaches critically depend on their software framework for discovery, execution, scheduling, and data orchestration. To address this challenge, we believe that a more agile and proactive software framework is essential to increase performance portability and improve user productivity. To this end, we have designed and implemented IRIS: a performance-portable framework for cross-platform heterogeneous computing. IRIS can discover available resources, manage multiple diverse programming platforms (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, OpenMP) simultaneously in the same execution, respect data dependencies, orchestrate data movement proactively, and provide for user-configurable scheduling. To simplify data movement, IRIS introduces a shared virtual device memory with relaxed consistency among different heterogeneous devices. IRIS also adds an automatic kernel workload partitioning technique using the polyhedral model so that it can resize kernels for a wide range of devices. Our evaluation on three architectures, ranging from Qualcomm Snapdragon to a Summit supercomputer node, shows that IRIS improves portability across a wide range of diverse heterogeneous architectures with negligible overhead.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Multibody for Everybody (M4E) - A Linearization Approach to Enable Frequency Domain Analysis, Time Integration and Control Co-Design

1.1 Background/Objectives: Marine energy represents a promising yet underexploited source of power. To increase the harvested power, significant efforts have been made to improve wave energy converter (WEC) modeling capabilities and optimize power take-off (PTO) performance; however, these efforts have often treated WEC dynamics, PTO design, and controller development sequentially. In contrast, control co-design (CCD) is emerging as a promising strategy to address these issues directly, creating a growing need for fast analysis tools suitable for repeated simulation and parametric studies [1]. To support this need, this work presents the Multibody for Everybody (M4E) [2] linearization module, which employs a symbolic toolbox to provide deeper insight of WEC design parameters. The objective is to demonstrate that a minimal-coordinate linearization of articulated WEC dynamics can provide accurate wave response predictions and substantial computational savings relative to nonlinear time-domain simulation, while preserving compatibility with broader wave-energy analysis workflows, enabling CCD. 1.2 Approach/Activities: The proposed approach linearizes the equations of motion, generated by M4E, in minimal coordinates about a selected operating point and combines the resulting system with frequencydomain hydrodynamic terms to incorporate the reduced mass, damping, stiffness, and forcing operators. The linearized model is used for both impedance-based response amplitude operator (RAO) prediction and rapid regular-wave time integration. The methodology is demonstrated on a single-flap device and a FOSWEC configuration, with linearized M4E responses compared against the corresponding nonlinear M4E simulations and WEC-Sim results. Regular-wave time histories, RAO trends, and runtime differences are assessed. The framework is also compatible with broader wave-energy workflows, including coupling to WecOptTool, although that capability is not the focus of this work [3]. 1.3 Results/Lessons: The linearized M4E model reproduces key regularwave response characteristics such as integration and Response Amplitude over multiple frequencies. This module matches nonlinear M4E and WEC-Sim results while substantially reducing integration cost. Thus, the proposed framework can serve as a rapid analysis layer for articulated WEC design, parameter studies, and controls-oriented workflows. The analysis is most appropriate in the near-equilibrium regime, about the linearization point.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Mini-Ckpts: Surviving OS Failures in Persistent Memory

Concern is growing in the high-performance computing (HPC) community on the reliability of future extreme-scale systems. Current efforts have focused on application fault-tolerance rather than the operating system (OS), despite the fact that recent studies have suggested that failures in OS memory are more likely. The OS is critical to a system's correct and efficient operation of the node and processes it governs -- and in HPC also for any other nodes a parallelized application runs on and communicates with: Any single node failure generally forces all processes of this application to terminate due to tight communication in HPC. Therefore, the OS itself must be capable of tolerating failures. In this work, we introduce mini-ckpts, a framework which enables application survival despite the occurrence of a fatal OS failure or crash. Mini-ckpts achieves this tolerance by ensuring that the critical data describing a process is preserved in persistent memory prior to the failure. Following the failure, the OS is rejuvenated via a warm reboot and the application continues execution effectively making the failure and restart transparent. The mini-ckpts rejuvenation and recovery process is measured to take between three to six seconds and has a failure-free overhead of between 3-5% for a number of key HPC workloads. In contrast to current fault-tolerance methods, this work ensures that the operating and runtime system can continue in the presence of faults. This is a much finer-grained and dynamic method of fault-tolerance than the current, coarse-grained, application-centric methods. Handling faults at this level has the potential to greatly reduce overheads and enables mitigation of additional fault scenarios.

Fiala, David↗
DOE OSTI2017

DINO: Divergent node cloning for sustained redundancy in HPC

Complexity and scale of next generation HPC systems pose significant challenges in fault resilience methods such that contemporary checkpoint/restart (C/R) methods that address fail-stop behavior may be insufficient. Redundant computing has been proposed as an alternative at extreme scale. Triple redundancy has an advantage over C/R in that it can also detect silent data corruption (SDC) and then correct results via voting. However, current redundant computing approaches do not repair failed or corrupted replicas. Consequently, SDCs can no longer be detected after a replica failure since the system has been degraded to dual redundancy without voting capability. Hence, a job may have to be aborted if voting uncovers mismatching results between the remaining two replicas. And while replicas are logically equivalent, they may have divergent runtime states during job execution, which presents a challenge to simply creating new replicas dynamically. This problem is addressed by, DIvergent NOde cloning (DINO), a redundant execution environment that quickly recovers from hard failures. DINO consists of a novel node cloning service integrated into the MPI runtime system that solves the problem of consolidating divergent states among replicas on-the-fly. With DINO, after degradation to dual redundancy, a good replica can be quickly cloned so that triple redundancy is restored. We present experimental results over 9 NAS Parallel Benchmarks (NPB), Sweep3D and LULESH. Results confirm the applicability of the approach and the correctness of the recovery process and indicate that DINO can recover from failures nearly instantly. The cloning overhead depends on the process image size that needs to be transferred between source and destination of the clone operation and varies between 5.60 to 90.48 s. Here, simulation results with our model show that dual redundancy with DINO recovery always outperforms 2x and surpasses 3x redundancy on up to 1 million nodes. To the best of our knowledge, the design and implementation for repairing failed replicas in redundant MPI computing is unprecedented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mechanical ventilation and indoor air quality in recently constructed U.S. homes in marine and cold-dry climates

Data were collected to characterize whole-house mechanical ventilation (WHMV) and indoor air quality (IAQ) in 55 homes in the Marine climate of Oregon and the Cold-Dry climate of Colorado in the U.S. Sixteen homes were monitored for two weeks, with and without WHMV operating. Ventilation airflows; airtightness; time-resolved CO 2 , PM2.5 and radon; and time-integrated NO 2 , NO X and formaldehyde were measured. Participants provided information about IAQ-impacting activities, perceptions and ventilation use. All homes had operational cooktop ventilation and bathroom exhaust. Thirty homes had equipment that could meet the ASHRAE 62.2–2010 standard with continuous or controlled runtime and 34 had some WHMV operating as found. Thirty-five of 46 participants with WHMV reported they did not know how to operate it, and only half of the systems were properly labeled. Two-week homes had lower formaldehyde, radon, CO 2 and NO (NO X -NO 2 ) when operated with WHMV, and had faster PM 2.5 decays following indoor emission events. Overall IAQ satisfaction was similar in Oregon and Colorado, but more Colorado participants (19% vs 3%) felt their IAQ could be improved and more reported dryness as a problem (58% vs. 14%). The collected data indicate that there are benefits of operating WHMV, even when continuous use may not be needed because outdoor pollutant concentrations are low and indoor sources do not present substantial challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Mechanical Ventilation and Indoor Air Quality in Recently Constructed U.S. Homes in Marine and Cold-Dry Climates Data from Building America Project

Data were collected to characterize whole-house mechanical ventilation (WHMV) and indoor air quality (IAQ) in 55 homes in the Marine climate of Oregon and Cold-Dry climate of Colorado in the U.S. Sixteen homes were monitored for two weeks, with and without WHMV operating. Ventilation airflows; airtightness; time-resolved CO2, PM2.5 and radon; and time-integrated NO2, NOX and formaldehyde were measured. Participants provided information about IAQ-impacting activities, perceptions and ventilation use. All homes had operational cooktop ventilation and bathroom exhaust. Thirty homes had equipment that could meet the ASHRAE 62.2-2010 standard with continuous or controlled runtime and 34 had some WHMV operating as found. Thirty-five of 46 participants with WHMV reported they did not know how to operate it, and only half of the systems were properly labeled. Two-week homes had lower formaldehyde, radon, CO2, and NO (NOX-NO2) when operated with WHMV; and also had faster PM2.5 decays following indoor emission events. Overall IAQ satisfaction was similar in Oregon and Colorado, but more Colorado participants (19 vs. 3%) felt their IAQ could be improved and more reported dryness as a problem (58 vs. 14%). The collected data indicate that there are benefits of operating WHMV, even when continuous use may not be needed because outdoor pollutant concentrations are low and indoor sources do not present substantial challenges.

air quality↗