Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “RESPONSE FUNCTIONS”

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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The PTPN2/PTPN1 inhibitor ABBV-CLS-484 unleashes potent anti-tumour immunity

Immune checkpoint blockade is effective for some patients with cancer, but most are refractory to current immunotherapies and new approaches are needed to overcome resistance. The protein tyrosine phosphatases PTPN2 and PTPN1 are central regulators of inflammation, and their genetic deletion in either tumour cells or immune cells promotes anti-tumour immunity. However, phosphatases are challenging drug targets; in particular, the active site has been considered undruggable. Here we present the discovery and characterization of ABBV-CLS-484 (AC484), a first-in-class, orally bioavailable, potent PTPN2 and PTPN1 active-site inhibitor. AC484 treatment in vitro amplifies the response to interferon and promotes the activation and function of several immune cell subsets. In mouse models of cancer resistant to PD-1 blockade, AC484 monotherapy generates potent anti-tumour immunity. We show that AC484 inflames the tumour microenvironment and promotes natural killer cell and CD8 + T cell function by enhancing JAK–STAT signalling and reducing T cell dysfunction. Inhibitors of PTPN2 and PTPN1 offer a promising new strategy for cancer immunotherapy and are currently being evaluated in patients with advanced solid tumours (ClinicalTrials.gov identifier NCT04777994). More broadly, our study shows that small-molecule inhibitors of key intracellular immune regulators can achieve efficacy comparable to or exceeding that of antibody-based immune checkpoint blockade in preclinical models. Finally, to our knowledge, AC484 represents the first active-site phosphatase inhibitor to enter clinical evaluation for cancer immunotherapy and may pave the way for additional therapeutics that target this important class of enzymes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Ionic diffusioosmotic transport in nanochannels grafted with pH-responsive polyelectrolyte brushes modeled using augmented strong stretching theory

In this paper, we study the diffusioosmotic (DOS) transport in a nanochannel grafted with pH-responsive polyelectrolyte (PE) brushes and establish brush-functionalization-driven enhancement in induced nanofluidic electric field and electrokinetic transport. The PE brushes are modelled using our recently developed augmented strong stretching theory (SST). We consider the generation of the DOS transport due to the imposition of a salt concentration gradient along the length of the nanochannel. The presence of the salt concentration gradient induces an electric field that has an osmotic (associated with the flow-driven migration of the ions in the induced electric double layer) and an ionic (associated with the conduction current) component. These two components evolve in a manner such that the electric field in the brush-grafted nanochannel is larger (smaller) in magnitude than that in the brush-less nanochannels for the case where the electric field is positive (negative). Furthermore, we quantify the DOS flow velocity and establish that for most of the parameter choices, the DOS velocity, which is a combination of the induced pressure-gradient-driven chemiosmotic component and the induced electric field driven electroosmotic transport, is significantly larger for the nanochannels grafted with backbone-charged PE brushes (i.e., brushes where the charge is distributed along the entire length of the brushes) as compared to brush-free nanochannels or nanochannels grafted with PE brushes containing charges on their non-grafted ends.

42 ENGINEERING↗
DOE OSTI2022

A distinct RNA recognition mechanism governs Np 4 decapping by RppH

Dinucleoside tetraphosphates, often described as alarmones because their cellular concentration increases in response to stress, have recently been shown to function in bacteria as precursors to nucleoside tetraphosphate (Np 4 ) RNA caps. Removal of this cap is critical for initiating 5' end-dependent degradation of those RNAs, potentially affecting bacterial adaptability to stress; however, the predominant Np 4 decapping enzyme in proteobacteria, ApaH, is inactivated by the very conditions of disulfide stress that enable Np 4 -capped RNAs to accumulate to high levels. Here, in this study, we show that, in Escherichia coli cells experiencing such stress, the RNA pyrophosphohydrolase RppH assumes a leading role in decapping those transcripts, preferring them as substrates over their triphosphorylated and diphosphorylated counterparts. Unexpectedly, this enzyme recognizes Np 4 -capped 5' ends by a mechanism distinct from the one it uses to recognize other 5' termini, resulting in a one-nucleotide shift in substrate specificity. The unique manner in which capped substrates of this kind bind to the active site of RppH positions the δ-phosphate, rather than the β-phosphate, for hydrolytic attack, generating triphosphorylated RNA as the primary product of decapping. Consequently, a second RppH-catalyzed deprotection step is required to produce the monophosphorylated 5' terminus needed to stimulate rapid RNA decay. The unconventional manner in which RppH recognizes Np 4 -capped 5' ends and its differential impact on the rates at which such termini are deprotected as a prelude to RNA degradation could have major consequences for reprogramming gene expression during disulfide stress.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Divergent selection and climate adaptation fuel genomic differentiation between sister species of Sphagnum (peat moss)

Abstract Background and Aims New plant species can evolve through the reinforcement of reproductive isolation via local adaptation along habitat gradients. Peat mosses (Sphagnaceae) are an emerging model system for the study of evolutionary genomics and have well-documented niche differentiation among species. Recent molecular studies have demonstrated that the globally distributed species Sphagnum magellanicum is a complex of morphologically cryptic lineages that are phylogenetically and ecologically distinct. Here, we describe the architecture of genomic differentiation between two sister species in this complex known from eastern North America: the northern S. diabolicum and the largely southern S. magniae. Methods We sampled plant populations from across a latitudinal gradient in eastern North America and performed whole genome and restriction-site associated DNA sequencing. These sequencing data were then analyzed computationally. Key Results Using sliding-window population genetic analyses we find that differentiation is concentrated within ‘islands’ of the genome spanning up to 400 kb that are characterized by elevated genetic divergence, suppressed recombination, reduced nucleotide diversity and increased rates of non-synonymous substitution. Sequence variants that are significantly associated with genetic structure and bioclimatic variables occur within genes that have functional enrichment for biological processes including abiotic stress response, photoperiodism and hormone-mediated signalling. Demographic modelling demonstrates that these two species diverged no more than 225 000 generations ago with secondary contact occurring where their ranges overlap. Conclusions We suggest that this heterogeneity of genomic differentiation is a result of linked selection and reflects the role of local adaptation to contrasting climatic zones in driving speciation. This research provides insight into the process of speciation in a group of ecologically important plants and strengthens our predictive understanding of how plant populations will respond as Earth’s climate rapidly changes.

58 GEOSCIENCES↗
DOE OSTI2020

Tunable Magnon Interactions in a Ferromagnetic Spin-1 Chain

NiNb 2 O 6 is an almost ideal realization of a 1D spin-1 ferromagnetic Heisenberg chain compound with weak unidirectional anisotropy. Using time-domain THz spectroscopy, we measure the low-energy electrodynamic response of NiNb 2 O 6 as a function of temperature and external magnetic field. At low temperatures, we find a magnonlike spin excitation, which corresponds to the lowest energy excitation at q ~ 0. At higher temperatures, we unexpectedly observe a temperature-dependent renormalization of the spin-excitation energy, which has a strong dependence on field direction. Using theoretical arguments, exact diagonalizations, and finite temperature dynamical Lanczos calculations, we construct a picture of magnon-magnon interactions that naturally explains the observed renormalization. In this work, we show how magnetic field strength and direction may be used to directly tune the sign of the magnon-magnon interaction. This unique scenario is a consequence of the spin-1 nature and has no analog in the more widely studied spin-1/2 systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Experimental testing of a prototype cantilevered liquid-nitrogen-cooled silicon mirror

This report presents testing of a prototype cantilevered liquid-nitrogen-cooled silicon mirror. This mirror was designed to be the first mirror for the new soft X-ray beamlines to be built as part of the Advanced Light Source Upgrade. Test activities focused on fracture, heat transfer, modal response and distortion, and indicated that the mirror functions as intended.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Mechanisms and functions of membrane lipid remodeling in plants

Lipid remodeling, defined herein as post-synthetic structural modifications of membrane lipids, play crucial roles in regulating the physicochemical properties of cellular membranes and hence their many functions. Processes affected by lipid remodeling include lipid metabolism, membrane repair, cellular homeostasis, fatty acid trafficking, cellular signaling and stress tolerance. Glycerolipids are the major structural components of cellular membranes and their composition can be adjusted by modifying their head groups, their acyl chain lengths and the number and position of double bonds. This focused review summarizes recent advances in our understanding of mechanisms of membrane lipid remodeling with emphasis on the lipases and acyltransferases involved in the modification of phosphatidylcholine and monogalactosyldiacylglycerol, the major membrane lipids of extraplastidic and photosynthetic membranes, respectively. We also discuss the role of triacylglycerol metabolism in membrane acyl chain remodeling. Finally, we discuss emerging data concerning the functional roles of glycerolipid remodeling in plant stress responses. Illustrating the molecular basis of lipid remodeling may lead to novel strategies for crop improvement and other biotechnological applications such as bioenergy production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Lipo-Chitooligosaccharides Induce Specialized Fungal Metabolite Profiles That Modulate Bacterial Growth

Lipo-chitooligosaccharides (LCOs) are historically known for their role as microbial-derived signaling molecules that shape plant symbiosis with beneficial rhizobia or mycorrhizal fungi. Recent studies showing that LCOs are widespread across the fungal kingdom have raised questions about the ecological function of these compounds in organisms that do not form symbiotic relationships with plants. To elucidate the ecological function of these compounds, we investigate the metabolomic response of the ubiquitous human pathogen Aspergillus fumigatus to LCOs. Our metabolomics data revealed that exogenous application of various types of LCOs to A. fumigatus resulted in significant shifts in the fungal metabolic profile, with marked changes in the production of specialized metabolites known to mediate ecological interactions. Using network analyses, we identify specific types of LCOs with the most significant effect on the abundance of known metabolites. Extracts of several LCO-induced metabolic profiles significantly impact the growth rates of diverse bacterial species. These findings suggest that LCOs may play an important role in the competitive dynamics of non-plant-symbiotic fungi and bacteria. This study identifies specific metabolomic profiles induced by these ubiquitously produced chemicals and creates a foundation for future studies into the potential roles of LCOs as modulators of interkingdom competition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

High Fidelity Modeling of WELL58-32 DFITS

Between 2017 and 2019, a series of injection tests were performed at pilot vertical well 58-32 at the Utah FORGE site. These injection activities were conducted in three distinct zones, isolated with packers and bridge plugs during the tests. Zone 1 consists of an open-hole section at the toe of the well, whereas Zones 2 and 3 are three-meter long cased and perforated sections. Several injection cycles were conducted in each zone. A detailed description of the geology of the FORGE site, of well 58-32 and of all injection cycles can be found in (Xing et al., 2020). The injection cycles included Diagnostic Fracture Injection Tests (DFITs). DFITs comprise two primary stages: an injection (pump-in) phase, where controlled fluid injection is employed to create a small hydraulic fracture in the surrounding rock, followed by a shut-in phase, where injection ceases, allowing observation of the formation’s pressure response. Utilizing methodologies such as the G-function analysis, DFITs provide valuable insights, including fracture closure pressure and the Instantaneous Shut-in Pressure (ISIP) and can be employed to estimate the minimum horizontal stress.

42 ENGINEERING↗
DOE OSTI2024

Nanoporous Materials Genome Center Final Technical Report

Nanoporous materials (NPMs), including zeolites/zeotypes, metal-organic frameworks (MOFs), covalent organic frameworks, polymers with intrinsic microporosity, and molecular cages, possess enormous potential in diverse areas relevant to the DOE Office of Science Basic Energy Sciences (BES) mission and objectives. The Nanoporous Materials Genome Center (NMGC) has developed exascale-ready software, computational/theoretical chemistry methods, and data-driven science approaches that enable (i) the de-novo design of functional NPMs for chemical separation and catalysis tasks of increasing complexity, (ii) the discovery of the most promising functional NPMs from databases of synthesized and hypothetical adsorbent structures and the optimization of process conditions for specific applications, and (iii) the microscopic-level understanding of the fundamental interactions underlying the function of NPMs including hierarchical architectures, composite materials, responsive frameworks that may undergo phase transitions or post-synthetic modifications, and materials containing defects, partial disorder, or interfaces. A pivotal part of the NMGC project has been a tight collaboration between leading experimental groups for synthesis and characterization of NPMs and of computational groups that allowed for iterative feedback. The NMGC project has resulted in the publication of more than 290 research and review articles including more than 60 publications in high-impact journals and more than 15 journal covers. NMGC publications have already received more than 20,000 citations (with more than 3,000 citations per year in 2021, 2022, and 2023) and contribute to an h-index of more than 72. The NMGC award has supported collaborative research involving 28 research groups and contributed to the training of more than 40 postdocs, more than 60 graduate students, and more than 20 undergraduate students with broad expertise in data-driven science approaches, computational chemistry methods, and high-performance computing, in addition to the skills to thrive in an integrated experimental and computational research environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

PET Imaging Agents (FES, FFNP, and FDHT) for Estrogen, Androgen, and Progesterone Receptors to Improve Management of Breast and Prostate Cancers by Functional Imaging

Many breast and prostate cancers are driven by the action of steroid hormones on their cognate receptors in primary tumors and in metastases, and endocrine therapies that inhibit hormone production or block the action of these receptors provide clinical benefit to many but not all of these cancer patients. Because it is difficult to predict which individuals will be helped by endocrine therapies and which will not, positron emission tomography (PET) imaging of estrogen receptor (ER) and progesterone receptor (PgR) in breast cancer, and androgen receptor (AR) in prostate cancer can provide useful, often functional, information on the likelihood of endocrine therapy response in individual patients. This review covers our development of three PET imaging agents, 16α-[ 18 F]fluoroestradiol (FES) for ER, 21-[ 18 F]fluoro-furanyl-nor-progesterone (FFNP) for PgR, and 16β-[ 18 F]fluoro-5α-dihydrotestosterone (FDHT) for AR, and the evolution of their clinical use. For these agents, the pathway from concept through development tracks with an emerging understanding of critical performance criteria that is needed for successful PET imaging of these low-abundance receptor targets. Progress in the ongoing evaluation of what they can add to the clinical management of breast and prostate cancers reflects our increased understanding of these diseases and of optimal strategies for predicting the success of clinical endocrine therapies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

08 HYDROGEN↗
DOE OSTI2023

Vibration Analysis of a Unimorph Nanobeam with a Dielectric Layer of Both Flexoelectricity and Piezoelectricity

In this study, for the first time, free and forced vibrational responses of a unimorph nanobeam consisting of a functionally graded base, along with a dielectric layer of both piezoelectricity and flexoelectricity, is investigated based on paradox-free local/nonlocal elasticity. The formulation and boundary conditions are attained by utilizing the energy method Hamilton’s principle. In order to set a comparison, the formulation of a model in the framework of differential nonlocal is first presented. An effective implementation of the generalized differential quadrature method (GDQM) is then utilized to solve higher-order partial differential equations. This method can be utilized to solve the complex equations whose analytic results are quite difficult to obtain. Lastly, the impact of various parameters is studied to characterize the vibrational behavior of the system. Additionally, the major impact of flexoelectricity compared to piezoelectricity on a small scale is exhibited. The results show that small-scale flexoelectricity, rather than piezoelectricity, is dominant in electromechanical coupling. One of the results that can be mentioned is that the beams with higher nonlocality have the higher voltage and displacement under the same excitation amplitude. The findings can be helpful for further theoretical as well as experimental studies in which dielectric material is used in smart structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Metasurfaces with Multipolar Resonances and Enhanced Light–Matter Interaction

Metasurfaces, composed of engineered nanoantennas, enable unprecedented control over electromagnetic waves by leveraging multipolar resonances to tailor light–matter interactions. This review explores key physical mechanisms that govern their optical properties, including the role of multipolar resonances in shaping metasurface responses, the emergence of bound states in the continuum (BICs) that support high-quality factor modes, and the Purcell effect, which enhances spontaneous emission rates at the nanoscale. These effects collectively underpin the design of advanced photonic devices with tailored spectral, angular, and polarization-dependent properties. This review discusses recent advances in metasurfaces and applications based on them, highlighting research that employs full-wave numerical simulations, analytical and semi-analytic techniques, multipolar decomposition, nanofabrication, and experimental characterization to explore the interplay of multipolar resonances, bound and quasi-bound states, and enhanced light–matter interactions. A particular focus is given to metasurface-enhanced photodetectors, where structured nanoantennas improve light absorption, spectral selectivity, and quantum efficiency. By integrating metasurfaces with conventional photodetector architectures, it is possible to enhance responsivity, engineer photocarrier generation rates, and even enable functionalities such as polarization-sensitive detection. The interplay between multipolar resonances, BICs, and emission control mechanisms provides a unified framework for designing next-generation optoelectronic devices. This review consolidates recent progress in these areas, emphasizing the potential of metasurface-based approaches for high-performance sensing, imaging, and energy-harvesting applications.

Kerker effect↗
DOE OSTI2025

Replication Data for: Deconstructing Chirality: Probing Local and Non-local Effects in Azobenzene Derivatives with X-ray Circular Dichroism

Resolving molecular chirality at the atomic scale remains a critical challenge in chemistry. Conventional Optical Circular Dichroism spectroscopy often overlooks subtle and localized structural features. Here, we computationally investigate site-specific X-ray Circular Dichroism (XCD) across a series of trans-azobenzene derivatives to deconstruct and interpret chiroptical signals at the atomic level. Our modeling reveals that XCD is capable of distinguishing dichroic contributions arising from both a local chiral center and global molecular twist, revealing their intricate interplay and potential for constructive or destructive interference. We show that sterically-induced global distortions can dominate the XCD signal in some cases, even suppressing the response from the chiral center itself. This insight suggests a new molecular design principle for tuning chiroptical activity, which we extend by proposing strategies to achieve unidirectional photoisomerization through steric gearing. Altogether, this work establishes a quantitative framework for engineering chiroptical responses, laying the foundation for the design of functional chiral systems utilizing principles of unidirectional molecular motor-like conformational dynamics.

Chemistry↗
DOE OSTI2023

Demonstrating Load-Shaping Capabilities of Cost Minimizing Heat Pump Water Heater Controls with Varying Price Profiles

Increased penetration of photovoltaics and electrification of traditionally gas appliances are exacerbating existing challenges in cost-effectively balancing electricity grid supply and demand. Decarbonization without incurring expensive transmission and distribution system capacity increases requires shifting building loads from peak demand times to peak renewable production times. Grid operators are evaluating new ways of encouraging load shifting, including using time-varying price structures to provide a financial incentive. If devices incorporate price-responsive controls, a price profile could be designed to yield a wide variety of load curves as needed to optimize grid functionality. Heat pump water heaters (HPWHs) are an ideal device for price-responsive controls because the storage tank enables them to optimize the timing of electricity consumption without impacting hot water delivery service. This paper presents work demonstrating how price-responsive controls for HPWHs can provide different load profiles, as needed to stabilize the grid, in response to different price profiles. HPWH manufacturers now include web API and CTA-2045 communication capabilities which enable sending load shaping control signals. Pilot studies and preliminary programs have utilized these capabilities with uniform control strategies to reduce 4-9 PM electricity consumption. However, no studies have developed flexible controls capable of both a) responding to constantly varying price profiles and b) customizing logic to match the needs of each HPWH. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls utilizing setpoint setting signals for fleets of HPWHs in response to varying price profiles. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of a residential, integrated HPWH manufacturer’s on-board controller. The proposed cost-reducing controls respond to constantly changing price profiles, providing the ability to change the price profile to generate load curves as needed to maintain grid stability. Preliminary simulations studying the load shaping capabilities of price-responsive controls on a fleet of 60 HPWHs have demonstrated an average of a) 135.4% increases in load during low-price periods, b) >36.8% reductions in electricity peak-price period, and c) 6.2% electricity cost savings.

24 POWER TRANSMISSION AND DISTRIBUTION↗